{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal:\n",
    "\n",
    "Search for anomalies in the time series of hotel room prices with unsupervised learning (no labeled data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.dates as md\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import host_subplot\n",
    "import mpl_toolkits.axisartist as AA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "from pyemma import msm\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.svm import OneClassSVM\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from pyemma import msm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "expedia = pd.read_csv('expedia_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104517    4733\n",
       "124342    4707\n",
       "68420     4580\n",
       "134154    4550\n",
       "40279     4535\n",
       "59781     4514\n",
       "137997    4504\n",
       "60846     4481\n",
       "49656     4460\n",
       "77089     4422\n",
       "116942    4419\n",
       "35223     4408\n",
       "38419     4374\n",
       "21018     4342\n",
       "60468     4339\n",
       "14082     4331\n",
       "59657     4327\n",
       "77795     4325\n",
       "24545     4290\n",
       "70177     4278\n",
       "37818     4270\n",
       "46274     4241\n",
       "94455     4227\n",
       "122112    4216\n",
       "78500     4176\n",
       "90845     4144\n",
       "131892    4002\n",
       "114932    3960\n",
       "117294    3949\n",
       "125083    3879\n",
       "          ... \n",
       "74069        1\n",
       "65873        1\n",
       "30050        1\n",
       "98625        1\n",
       "1519         1\n",
       "45083        1\n",
       "53271        1\n",
       "99264        1\n",
       "135241       1\n",
       "102457       1\n",
       "116169       1\n",
       "42491        1\n",
       "7192         1\n",
       "127020       1\n",
       "1008         1\n",
       "91586        1\n",
       "116182       1\n",
       "66212        1\n",
       "113011       1\n",
       "45062        1\n",
       "97334        1\n",
       "62836        1\n",
       "54641        1\n",
       "91100        1\n",
       "39255        1\n",
       "69676        1\n",
       "72761        1\n",
       "48140        1\n",
       "8565         1\n",
       "127084       1\n",
       "Name: prop_id, Length: 136886, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expedia['prop_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = expedia.loc[expedia['prop_id'] == 104517]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   4733.000\n",
       "mean     109.967\n",
       "std      124.661\n",
       "min        0.070\n",
       "25%       67.000\n",
       "50%       95.000\n",
       "75%      133.000\n",
       "max     5584.000\n",
       "Name: price_usd, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "df['price_usd'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "219    3487\n",
       "100     424\n",
       "220     184\n",
       "55      124\n",
       "216      78\n",
       "129      49\n",
       "59       37\n",
       "117      32\n",
       "99       25\n",
       "103      24\n",
       "92       23\n",
       "132      18\n",
       "50       16\n",
       "31       16\n",
       "15       12\n",
       "158      11\n",
       "137      11\n",
       "187      10\n",
       "56        8\n",
       "229       8\n",
       "2         7\n",
       "80        7\n",
       "205       7\n",
       "32        7\n",
       "85        7\n",
       "13        6\n",
       "73        6\n",
       "14        5\n",
       "212       5\n",
       "202       4\n",
       "       ... \n",
       "23        2\n",
       "33        2\n",
       "72        2\n",
       "215       2\n",
       "53        2\n",
       "77        2\n",
       "104       1\n",
       "37        1\n",
       "68        1\n",
       "223       1\n",
       "81        1\n",
       "113       1\n",
       "211       1\n",
       "203       1\n",
       "79        1\n",
       "47        1\n",
       "11        1\n",
       "214       1\n",
       "194       1\n",
       "186       1\n",
       "178       1\n",
       "162       1\n",
       "154       1\n",
       "138       1\n",
       "130       1\n",
       "70        1\n",
       "30        1\n",
       "149       1\n",
       "133       1\n",
       "18        1\n",
       "Name: visitor_location_country_id, Length: 73, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['visitor_location_country_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   4733.000\n",
       "mean       2.703\n",
       "std        1.757\n",
       "min        1.000\n",
       "25%        2.000\n",
       "50%        2.000\n",
       "75%        3.000\n",
       "max       28.000\n",
       "Name: srch_length_of_stay, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srch_length_of_stay'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   4733.000\n",
       "mean      48.443\n",
       "std       56.096\n",
       "min        0.000\n",
       "25%        9.000\n",
       "50%       28.000\n",
       "75%       64.000\n",
       "max      308.000\n",
       "Name: srch_booking_window, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srch_booking_window'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4120\n",
       "2     515\n",
       "3      74\n",
       "4      15\n",
       "6       5\n",
       "8       3\n",
       "5       1\n",
       "Name: srch_room_count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srch_room_count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['srch_room_count'] == 1]\n",
    "df = df.loc[df['visitor_location_country_id'] == 219]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2012-11-01 02:48:30', '2013-06-30 22:50:21')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date_time'].min(), df['date_time'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['date_time', 'price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3049 entries, 2041 to 9917395\n",
      "Data columns (total 4 columns):\n",
      "date_time                   3049 non-null object\n",
      "price_usd                   3049 non-null float64\n",
      "srch_booking_window         3049 non-null int64\n",
      "srch_saturday_night_bool    3049 non-null int64\n",
      "dtypes: float64(1), int64(2), object(1)\n",
      "memory usage: 119.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   3049.000\n",
       "mean     112.939\n",
       "std      113.374\n",
       "min        0.120\n",
       "25%       67.000\n",
       "50%      100.000\n",
       "75%      141.000\n",
       "max     5584.000\n",
       "Name: price_usd, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date_time'] = pd.to_datetime(df['date_time'])\n",
    "df = df.sort_values('date_time')\n",
    "df['price_usd'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we have detected one extreme anomaly which was the Max price_usd at 5584. If an individual data instance can be considered as anomalous with respect to the rest of the data, we call it Point Anomalies (e.g. purchase with large transaction value). We could go back to check the log to see what was it about. After a little bit investigation, I guess it was either a mistake or user seached a presidential suite by accident and had no intention to book or view. In order to find more anomalies that are not extreme, I decided to remove this one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>srch_id</th>\n",
       "      <th>date_time</th>\n",
       "      <th>site_id</th>\n",
       "      <th>visitor_location_country_id</th>\n",
       "      <th>visitor_hist_starrating</th>\n",
       "      <th>visitor_hist_adr_usd</th>\n",
       "      <th>prop_country_id</th>\n",
       "      <th>prop_id</th>\n",
       "      <th>prop_starrating</th>\n",
       "      <th>prop_review_score</th>\n",
       "      <th>...</th>\n",
       "      <th>comp6_rate_percent_diff</th>\n",
       "      <th>comp7_rate</th>\n",
       "      <th>comp7_inv</th>\n",
       "      <th>comp7_rate_percent_diff</th>\n",
       "      <th>comp8_rate</th>\n",
       "      <th>comp8_inv</th>\n",
       "      <th>comp8_rate_percent_diff</th>\n",
       "      <th>click_bool</th>\n",
       "      <th>gross_bookings_usd</th>\n",
       "      <th>booking_bool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2905344</th>\n",
       "      <td>195154</td>\n",
       "      <td>2013-04-07 20:59:07</td>\n",
       "      <td>5</td>\n",
       "      <td>219</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>219</td>\n",
       "      <td>104517</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000</td>\n",
       "      <td>...</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28.000</td>\n",
       "      <td>0</td>\n",
       "      <td>nan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         srch_id            date_time  site_id  visitor_location_country_id  \\\n",
       "2905344   195154  2013-04-07 20:59:07        5                          219   \n",
       "\n",
       "         visitor_hist_starrating  visitor_hist_adr_usd  prop_country_id  \\\n",
       "2905344                      nan                   nan              219   \n",
       "\n",
       "         prop_id  prop_starrating  prop_review_score      ...       \\\n",
       "2905344   104517                4              4.000      ...        \n",
       "\n",
       "         comp6_rate_percent_diff  comp7_rate  comp7_inv  \\\n",
       "2905344                      nan         nan        nan   \n",
       "\n",
       "         comp7_rate_percent_diff  comp8_rate  comp8_inv  \\\n",
       "2905344                      nan      -1.000      0.000   \n",
       "\n",
       "         comp8_rate_percent_diff  click_bool  gross_bookings_usd  booking_bool  \n",
       "2905344                   28.000           0                 nan             0  \n",
       "\n",
       "[1 rows x 54 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expedia.loc[(expedia['price_usd'] == 5584) & (expedia['visitor_location_country_id'] == 219)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[df['price_usd'] < 5584]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_csv('TimeSeriesExpedia.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   3048.000\n",
       "mean     111.144\n",
       "std       55.055\n",
       "min        0.120\n",
       "25%       67.000\n",
       "50%      100.000\n",
       "75%      141.000\n",
       "max      536.000\n",
       "Name: price_usd, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_usd'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing that Point Anomaly, we can at least visualize the rest of the data, and perhaps find out more anomalies in several ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "df.plot(x='date_time', y='price_usd', figsize=(12,6))\n",
    "plt.xlabel('Date time')\n",
    "plt.ylabel('Price in USD')\n",
    "plt.title('Time Series of room price by date time of search');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>srch_booking_window</th>\n",
       "      <th>srch_saturday_night_bool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3945840</th>\n",
       "      <td>2012-11-01 02:48:30</td>\n",
       "      <td>84.000</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63387</th>\n",
       "      <td>2012-11-01 03:06:43</td>\n",
       "      <td>78.000</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3352426</th>\n",
       "      <td>2012-11-01 09:04:18</td>\n",
       "      <td>114.000</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5257418</th>\n",
       "      <td>2012-11-01 09:11:03</td>\n",
       "      <td>76.000</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7091061</th>\n",
       "      <td>2012-11-01 10:15:25</td>\n",
       "      <td>128.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date_time  price_usd  srch_booking_window  \\\n",
       "3945840 2012-11-01 02:48:30     84.000                   19   \n",
       "63387   2012-11-01 03:06:43     78.000                   16   \n",
       "3352426 2012-11-01 09:04:18    114.000                   56   \n",
       "5257418 2012-11-01 09:11:03     76.000                   56   \n",
       "7091061 2012-11-01 10:15:25    128.000                    0   \n",
       "\n",
       "         srch_saturday_night_bool  \n",
       "3945840                         0  \n",
       "63387                           1  \n",
       "3352426                         1  \n",
       "5257418                         1  \n",
       "7091061                         1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAF3CAYAAADgjOwXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XmUldWZ7/Hvw2yEOIEGRYMxODBVCaioGUAbNV46JhrU4G0RzSVpjTFqDE7dTWLomFabmHgTL0YXmEUrNioaO7EbFYe0aKDoYpJo1KBWIAKKBmKMYPb9o96qlFBUFcOps4v6ftY665x3v+95z3N2sYpf7XfYkVJCkiRJeehQ7gIkSZL0V4YzSZKkjBjOJEmSMmI4kyRJyojhTJIkKSOGM0mSpIwYziRJkjJiOJMkScqI4UySJCkjhjNJkqSMdCp3ATuiZ8+eqW/fvuUuQ5IkqVlVVVVrU0q9mtuuTYezvn37smDBgnKXIUmS1KyIeKUl23lYU5IkKSOGM0mSpIwYziRJkjLSps85kySplDZu3EhNTQ3vvvtuuUtRG9KtWzf69OlD586dt+v9hjNJkraipqaGHj160LdvXyKi3OWoDUgp8cYbb1BTU8PBBx+8XfvwsKYkSVvx7rvvss8++xjM1GIRwT777LNDo62GM0mSmmAw07ba0X8zhjNJkjI2efJkBgwYwODBg6msrOTZZ58t6ed179692W2mTZtGhw4dWLx4cX3bwIEDWbFixU6p4Y477mDQoEEMHjyYgQMH8sADDzS5/ezZs3nuuecaXTdp0iQ+9KEPsXr16vq2ht/xuOOOa7aevn37snbt2i3aH3/8cZ5++ulm37+tPOdMkqQWmjLnhZ26v0tHHdrk+nnz5vHQQw+xcOFCunbtytq1a3nvvfd2+HM3bdpEp047FgH69OnD5MmTmTlz5g7X01BNTQ2TJ09m4cKF7LHHHmzYsIE1a9Y0+Z7Zs2czevRo+vfv3+j6nj17ctNNN/G9731vi3U7Eq4ef/xxunfv3qKAty0cOZMkKVOrVq2iZ8+edO3aFagNGfvvvz8AVVVVfPrTn2bo0KGcfPLJrFq1CoDbbruNo446ioqKCs444wzeeecdAM477zwuu+wyRo4cycSJE9mwYQPjx4+vH6G699576z/3mmuuoaKiguHDh/P66683Wtvo0aNZtmwZzz///Bbr7rrrLgYNGsTAgQOZOHFifXv37t2b3ffq1avp0aNH/ehW9+7d60+sb+y7Pf300zz44INcccUVVFZW8tJLL22xz/PPP5+ZM2fy5ptvbrGu7nP+8pe/cOGFFzJgwABGjx7NqaeeyqxZs+q3++EPf8iQIUMYNGgQv/71r1mxYgW33norU6ZMobKykqeeeqrRftoehjNJkjJ10kkn8dprr3HooYdy4YUX8sQTTwC1t/i4+OKLmTVrFlVVVZx//vlcc801AJx++unMnz+fRYsWccQRR3D77bfX7++FF17gkUce4aabbuK6665jjz32YMmSJSxevJgTTjgBgD/+8Y8MHz6cRYsW8alPfYrbbrut0do6dOjAN7/5Tf75n//5A+0rV65k4sSJPPbYY1RXVzN//nxmz57d4n1XVFSw3377cfDBBzN+/Hh+9rOf1a9r7Lsdd9xxfPazn+WGG26gurqaQw45ZIt9du/enfPPP5+bb755q3193333sWLFCpYsWcJPfvIT5s2b94H1PXv2ZOHChfz93/89N954I3379uUrX/kKl156KdXV1Xzyk5/c6r63leFMkqRMde/enaqqKqZOnUqvXr0466yzmDZtGs8//zxLly5l1KhRVFZW8p3vfIeamhoAli5dyic/+UkGDRrEjBkzWLZsWf3+xowZQ8eOHQF45JFHuOiii+rX7bXXXgB06dKF0aNHAzB06NAmzyMbO3YszzzzDL/97W/r2+bPn8+IESPo1asXnTp14pxzzuHJJ59s8b47duzIww8/zKxZszj00EO59NJLmTRpUrPfrTlf+9rXmD59On/4wx8aXf/LX/6SMWPG0KFDBz7ykY8wcuTID6w//fTTm6x7Z/KcM0mSMtaxY0dGjBjBiBEjGDRoENOnT2fo0KEMGDBgi9EdqD18OXv2bCoqKpg2bRqPP/54/brdd9+9/nVKqdGrCjt37lzf3rFjRzZt2rTV2jp16sTll1/+gXO5Ukpb3b6xfb///vsMHToUgM9+9rN8+9vfJiI4+uijOfrooxk1ahTjx49n0qRJTX635uy5556MHTuWH/3oR42ub6puoP7QcnN9sjMYztqplp7U2tzJqpKk0nn++efp0KED/fr1A6C6upqPfvSjHHbYYaxZs4Z58+Zx7LHHsnHjRl544QUGDBjA+vXr6d27Nxs3bmTGjBkccMABje77pJNO4pZbbuH73/8+AOvWrasfPdsW5513Hv/yL//C+vXrATjmmGO45JJLWLt2LXvttRd33XUXF1988Vbf37FjR6qrq+uXV65cye9//3uGDBnyge8MbPW79ejRo/7zm3LZZZdx1FFHNRquPvGJTzB9+nTGjRvHmjVrePzxxxk7dmyT++vRo8dWR+J2hIc1JUnK1IYNGxg3bhz9+/dn8ODBPPfcc0yaNIkuXbowa9YsJk6cSEVFBZWVlfVXHV533XUcc8wxjBo1isMPP3yr+7722mtZt24dAwcOpKKigrlz525XjV26dOFrX/ta/a0qevfuzXe/+11GjhxJRUUFQ4YM4bTTTmvx/jZu3Mg3vvENDj/8cCorK5k5c2b9uWJb+25nn302N9xwA0ceeWSjFwTU6dmzJ5///Of585//vMW6M844gz59+jBw4EC+/OUvc8wxx7DHHns0Wevf/u3fcv/99+/0CwKiuWG8nA0bNiwtWLCg3GW0SY6cSVLzli9fzhFHHFHuMtRKNmzYQPfu3XnjjTc4+uij+e///m8+8pGPbNe+Gvu3ExFVKaVhzb3Xw5qSJEnU3h7krbfe4r333uMf/uEftjuY7SjDmSRJEmzTBQal5DlnkiRJGTGcSZIkZcRwJkmSlBHDmSRJUkYMZ5IkZWzy5MkMGDCAwYMHU1lZybPPPlvSz6ubCLwpr7/+OqNHj6aiooL+/ftz6qmnNrn9W2+9tdU78wNEBJdffnn98o033lg/ZdOtt97KnXfe2eT+p02bxle/+tVG120+92db4NWakiS11Nzv7tz9jbyqydXz5s3joYceYuHChXTt2pW1a9fy3nvv7fDHbtq0iU6dtj8C/OM//iOjRo3ikksuAWDx4sVNbl8Xzi688MJG13ft2pX77ruPq666ip49e35g3Ve+8pXtrhNqw9nVV1+9Q/tobY6cSZKUqVWrVtGzZ8/6eR179uzJ/vvvD0BVVRWf/vSnGTp0KCeffDKrVq0C4LbbbuOoo46ioqKCM844g3feeQeonWbpsssuY+TIkUycOJENGzYwfvx4Bg0axODBg7n33nvrP/eaa66hoqKC4cOH8/rrrzdaV58+feqXBw8eDNTexPXEE09kyJAhDBo0iAceeACAK6+8kpdeeonKykquuOKKLfbXqVMnJkyYwJQpU7ZYN2nSJG688UagdlL1wYMHc+yxx3LFFVcwcODA+u1WrlzJKaecQr9+/fjmN79Z/7l/+tOfqKys5Jxzzmlpt5ed4UySpEyddNJJvPbaaxx66KFceOGFPPHEE0DtFEcXX3wxs2bNoqqqivPPP59rrrkGgNNPP5358+ezaNEijjjiCG6//fb6/b3wwgs88sgj3HTTTVx33XXsscceLFmyhMWLF3PCCScA8Mc//pHhw4ezaNEiPvWpT3HbbbdtUddFF13EBRdcwMiRI5k8eTIrV64EoFu3btx///0sXLiQuXPncvnll5NS4vrrr+eQQw6hurqaG264odHvetFFFzFjxgzefvvtrfbH+PHjufXWW5k3bx4dO3b8wLrq6mpmzpzJkiVLmDlzJq+99hrXX389u+22G9XV1cyYMWMber68PKwpSVKmunfvTlVVFU899RRz587lrLPO4vrrr2fYsGEsXbqUUaNGAfD+++/Tu3dvAJYuXcq1117LW2+9xYYNGzj55JPr9zdmzJj6UPPII49w991316+rm/S8S5cujB49GoChQ4cyZ86cLeo6+eSTefnll3n44Yf5xS9+wZFHHsnSpUvZc889ufrqq3nyySfp0KEDv/vd7xodeWvMhz/8Yc4991x+8IMfsNtuu22x/q233mL9+vUcd9xxAIwdO5aHHnqofv2JJ55YPxdm//79eeWVVzjwwANb9Nm5MZxJkpSxjh07MmLECEaMGMGgQYOYPn06Q4cOZcCAAcybN2+L7c877zxmz55NRUUF06ZN+8Bd73fffff61yklImKL93fu3Lm+vWPHjmzatKnRuvbee2/Gjh3L2LFjGT16NE8++STr169nzZo1VFVV0blzZ/r27cu7777b4u/69a9/nSFDhjB+/Pgt1jU3F3jdod/m6m4LPKwpSVKmnn/+eX7zm9/UL1dXV/PRj36Uww47jDVr1tSHs40bN7Js2TIA1q9fT+/evdm4cWOTh/JOOukkbrnllvrldevWtbiuxx57rP5ctvXr1/PSSy9x0EEH8fbbb7PvvvvSuXNn5s6dyyuvvAJAjx49WL9+fbP73XvvvTnzzDM/cCi2zl577UWPHj145plnAD4w6teUzp07s3HjxpZ+tSwYziRJytSGDRsYN24c/fv3Z/DgwTz33HNMmjSJLl26MGvWLCZOnEhFRQWVlZU8/fTTAFx33XUcc8wxjBo1isMPP3yr+7722mtZt24dAwcOpKKigrlz57a4rqqqKoYNG1Z/cv6XvvQljjrqKM455xwWLFjAsGHDmDFjRv3n77PPPhx//PEMHDiw0QsCGrr88stZu3Zto+tuv/12JkyYwLHHHktKqf4wZlMmTJjA4MGD29QFAdHcMGHOhg0blhYsWFDuMtqkKXNeaNF2l446tMSVSFK+li9fzhFHHFHuMlTYsGFD/X3Yrr/+elatWsXNN99c5qoa19i/nYioSikNa+69JRs5i4huEfGriFgUEcsi4ltF+8ER8WxE/CYiZkZEl6K9a7H8YrG+b6lqkyRJbc9//Md/UFlZycCBA3nqqae49tpry11SSZTygoA/AyeklDZERGfglxHxC+AyYEpK6e6IuBW4APhx8bwupfTxiDgb+B5wVgnrkyRJbchZZ53FWWft+tGgZCNnqdaGYrFz8UjACcCson068Lni9WnFMsX6E6Oxy0gkSZJ2YSW9ICAiOkZENbAamAO8BLyVUqq7vrUGOKB4fQDwGkCx/m1gn1LWJ0lSc9ryudkqjx39N1PScJZSej+lVAn0AY4GGjursu4bNDZKtsW3i4gJEbEgIhasWbNm5xUrSdJmunXrxhtvvGFAU4ullHjjjTfo1q3bdu+jVW5Cm1J6KyIeB4YDe0ZEp2J0rA+wstisBjgQqImITsAewJuN7GsqMBVqr9ZshfIlSe1Unz59qKmpwcEAbYtu3bp9YO7RbVWycBYRvYCNRTDbDfgbak/ynwt8AbgbGAc8ULzlwWJ5XrH+seSfKpKkMurcuTMHH3xwuctQO1PKkbPewPSI6Ejt4dN7UkoPRcRzwN0R8R3gf4C62wDfDvw0Il6kdsTs7BLWJkmSlKWShbOU0mLgyEbaX6b2/LPN298FxpSqHkmSpLbA6ZskSZIyYjiTJEnKiOFMkiQpI4YzSZKkjBjOJEmSMmI4kyRJyojhTJIkKSOGM0mSpIwYziRJkjJiOJMkScqI4UySJCkjhjNJkqSMGM4kSZIyYjiTJEnKiOFMkiQpI4YzSZKkjBjOJEmSMmI4kyRJyojhTJIkKSOGM0mSpIwYziRJkjJiOJMkScqI4UySJCkjhjNJkqSMGM4kSZIyYjiTJEnKiOFMkiQpI4YzSZKkjBjOJEmSMmI4kyRJyojhTJIkKSOGM0mSpIwYziRJkjJiOJMkScqI4UySJCkjhjNJkqSMGM4kSZIyYjiTJEnKiOFMkiQpIyULZxFxYETMjYjlEbEsIi4p2idFxO8iorp4nNrgPVdFxIsR8XxEnFyq2iRJknLVqYT73gRcnlJaGBE9gKqImFOsm5JSurHhxhHRHzgbGADsDzwSEYemlN4vYY2SJElZKdnIWUppVUppYfF6PbAcOKCJt5wG3J1S+nNK6bfAi8DRpapPkiQpR61yzllE9AWOBJ4tmr4aEYsj4o6I2KtoOwB4rcHbamg6zEmSJO1ySh7OIqI7cC/w9ZTSH4AfA4cAlcAq4Ka6TRt5e2pkfxMiYkFELFizZk2JqpYkSSqPkoaziOhMbTCbkVK6DyCl9HpK6f2U0l+A2/jrocsa4MAGb+8DrNx8nymlqSmlYSmlYb169Spl+ZIkSa2ulFdrBnA7sDyl9K8N2ns32OzzwNLi9YPA2RHRNSIOBvoBvypVfZIkSTkq5dWaxwN/ByyJiOqi7WrgixFRSe0hyxXAlwFSSssi4h7gOWqv9LzIKzUlSVJ7U7JwllL6JY2fR/bzJt4zGZhcqpq07abMeaHZbS4ddWgrVCJJUvvgDAGSJEkZMZxJkiRlxHAmSZKUEcOZJElSRgxnkiRJGTGcSZIkZcRwJkmSlBHDmSRJUkYMZ5IkSRkxnEmSJGXEcCZJkpQRw5kkSVJGDGeSJEkZMZxJkiRlxHAmSZKUEcOZJElSRgxnkiRJGTGcSZIkZcRwJkmSlBHDmSRJUkYMZ5IkSRkxnEmSJGXEcCZJkpQRw5kkSVJGDGeSJEkZMZxJkiRlxHAmSZKUEcOZJElSRgxnkiRJGTGcSZIkZcRwJkmSlBHDmSRJUkYMZ5IkSRnpVO4CtPNNmfNCuUuQJEnbyZEzSZKkjBjOJEmSMmI4kyRJyojhTJIkKSOGM0mSpIwYziRJkjJSsnAWEQdGxNyIWB4RyyLikqJ974iYExG/KZ73KtojIn4QES9GxOKIGFKq2iRJknJVypGzTcDlKaUjgOHARRHRH7gSeDSl1A94tFgG+AzQr3hMAH5cwtokSZKyVLJwllJalVJaWLxeDywHDgBOA6YXm00HPle8Pg24M9V6BtgzInqXqj5JkqQctco5ZxHRFzgSeBbYL6W0CmoDHLBvsdkBwGsN3lZTtG2+rwkRsSAiFqxZs6aUZUuSJLW6koeziOgO3At8PaX0h6Y2baQtbdGQ0tSU0rCU0rBevXrtrDIlSZKyUNJwFhGdqQ1mM1JK9xXNr9cdriyeVxftNcCBDd7eB1hZyvokSZJyU8qrNQO4HVieUvrXBqseBMYVr8cBDzRoP7e4anM48Hbd4U9JkqT2olMJ93088HfAkoioLtquBq4H7omIC4BXgTHFup8DpwIvAu8A40tYmyRJUpZKFs5SSr+k8fPIAE5sZPsEXFSqeiRJktoCZwiQJEnKiOFMkiQpI4YzSZKkjBjOJEmSMmI4kyRJyojhTJIkKSOGM0mSpIwYziRJkjJiOJMkScqI4UySJCkjhjNJkqSMtCicRcTxLWmTJEnSjmnpyNkPW9gmSZKkHdCpqZURcSxwHNArIi5rsOrDQMdSFiZJktQeNRnOgC5A92K7Hg3a/wB8oVRFSZIktVdNhrOU0hPAExExLaX0SivVJEmS1G41N3JWp2tETAX6NnxPSumEUhQlSZLUXrU0nP07cCvwE+D90pUjSZLUvrU0nG1KKf24pJVIkiSpxbfS+FlEXBgRvSNi77pHSSuTJElqh1o6cjaueL6iQVsCPrZzy5EkSWrfWhTOUkoHl7oQSZIktTCcRcS5jbWnlO7cueVIkiS1by09rHlUg9fdgBOBhYDhTJIkaSdq6WHNixsuR8QewE9LUpEkSVI71tKrNTf3DtBvZxYiSZKklp9z9jNqr86E2gnPjwDuKVVRkiRJ7VVLzzm7scHrTcArKaWaEtQjSZLUrrXosGYxAfqvgR7AXsB7pSxKkiSpvWpROIuIM4FfAWOAM4FnI+ILpSxMkiSpPWrpYc1rgKNSSqsBIqIX8Agwq1SFSZIktUctvVqzQ10wK7yxDe+VJElSC7V05OzhiPhP4K5i+Szg56UpSZIkqf1qMpxFxMeB/VJKV0TE6cAngADmATNaoT5JkqR2pblDk98H1gOklO5LKV2WUrqU2lGz75e6OEmSpPamuXDWN6W0ePPGlNICoG9JKpIkSWrHmgtn3ZpYt9vOLESSJEnNh7P5EfF/Nm+MiAuAqtKUJEmS1H41d7Xm14H7I+Ic/hrGhgFdgM+XsjBJkqT2qMmRs5TS6yml44BvASuKx7dSSsemlH7f1Hsj4o6IWB0RSxu0TYqI30VEdfE4tcG6qyLixYh4PiJO3pEvJUmS1Fa16D5nKaW5wNxt3Pc04Bbgzs3ap6SUGk6kTkT0B84GBgD7A49ExKEppfe38TMlSZLatJLd5T+l9CTwZgs3Pw24O6X055TSb4EXgaNLVZskSVKuyjEF01cjYnFx2HOvou0A4LUG29QUbZIkSe1Ka4ezHwOHAJXAKuCmoj0a2TY1toOImBARCyJiwZo1a0pTpSRJUpm0ajgrLjB4P6X0F+A2/nrosgY4sMGmfYCVW9nH1JTSsJTSsF69epW2YEmSpFbWquEsIno3WPw8UHcl54PA2RHRNSIOBvoBv2rN2iRJknLQoqs1t0dE3AWMAHpGRA3wT8CIiKik9pDlCuDLACmlZRFxD/AcsAm4yCs1JUlSe1SycJZS+mIjzbc3sf1kYHKp6pEkSWoLynG1piRJkrbCcCZJkpQRw5kkSVJGDGeSJEkZMZxJkiRlxHAmSZKUEcOZJElSRkp2nzOp1c397o7vY+RVO74PSZJ2gOFMbcfOCF+SJGXOw5qSJEkZMZxJkiRlxHAmSZKUEcOZJElSRgxnkiRJGTGcSZIkZcRwJkmSlBHDmSRJUkYMZ5IkSRkxnEmSJGXEcCZJkpQRw5kkSVJGnPhc2hbNTb4+8qrWqUOStMty5EySJCkjhjNJkqSMGM4kSZIyYjiTJEnKiBcESA01d8K/JEkl5siZJElSRgxnkiRJGfGwpnbYlDkvtGi7S0cdWuJKJElq+xw5kyRJyojhTJIkKSOGM0mSpIwYziRJkjJiOJMkScqI4UySJCkjhjNJkqSMeJ8ztZqW3A/Ne6FJkto7R84kSZIyUrKRs4i4AxgNrE4pDSza9gZmAn2BFcCZKaV1ERHAzcCpwDvAeSmlhaWqTSqZ5iZOH3lV69QhSWqzSjlyNg04ZbO2K4FHU0r9gEeLZYDPAP2KxwTgxyWsS5IkKVslC2cppSeBNzdrPg2YXryeDnyuQfudqdYzwJ4R0btUtUmSJOWqtc852y+ltAqgeN63aD8AeK3BdjVFmyRJUruSywUB0UhbanTDiAkRsSAiFqxZs6bEZUmSJLWu1g5nr9cdriyeVxftNcCBDbbrA6xsbAcppakppWEppWG9evUqabGSJEmtrbXD2YPAuOL1OOCBBu3nRq3hwNt1hz8lSZLak1LeSuMuYATQMyJqgH8CrgfuiYgLgFeBMcXmP6f2NhovUnsrjfGlqkuSJClnJQtnKaUvbmXViY1sm4CLSlWLJElSW+H0TW1IS6Y/asuGvzoV5u5T7jIkSSqrXK7WlCRJEoYzSZKkrBjOJEmSMmI4kyRJyojhTJIkKSOGM0mSpIx4Kw21muGvTi13CZIkZc+RM0mSpIwYziRJkjJiOJMkScqI4UySJCkjhjNJkqSMGM4kSZIyYjiTJEnKiOFMkiQpI4YzSZKkjBjOJEmSMmI4kyRJyohzawpoft7LZw6a0EqVSJLUvhnO1CKGN0mSWoeHNSVJkjJiOJMkScqI4UySJCkjhjNJkqSMGM4kSZIyYjiTJEnKiOFMkiQpI4YzSZKkjBjOJEmSMmI4kyRJyojTNykr815+o0XbHfuxfUpciSRJ5WE4k3Iz97tNrx95VevUIUkqCw9rSpIkZcRwJkmSlBHDmSRJUkYMZ5IkSRnxggDtFMNfnVruEiRJ2iUYzqTW1NyVmJKkdq8s4SwiVgDrgfeBTSmlYRGxNzAT6AusAM5MKa0rR32SJEnlUs5zzkamlCpTSsOK5SuBR1NK/YBHi2VJkqR2JacLAk4DphevpwOfK2MtkiRJZVGuc84S8F8RkYD/l1KaCuyXUloFkFJaFRH7lqk2tQEtmebJKZ4kSW1RucLZ8SmllUUAmxMRv27pGyNiAjAB4KCDDipVfZIkSWVRlsOaKaWVxfNq4H7gaOD1iOgNUDyv3sp7p6aUhqWUhvXq1au1SpYkSWoVrT5yFhG7Ax1SSuuL1ycB3wYeBMYB1xfPD7R2bdIuwYnTJalNK8dhzf2A+yOi7vP/LaX0cETMB+6JiAuAV4ExZahNkiSprFo9nKWUXgYqGml/AzixteuRJEnKSU630pAkSWr3DGeSJEkZMZxJkiRlxHAmSZKUEcOZJElSRgxnkiRJGSnX9E2StldzN5mVJLVphjPtspwcXZLUFnlYU5IkKSOOnEntzc44LOr8nJJUMo6cSZIkZcRwJkmSlBHDmSRJUkY856ydGP7q1HKX0KZ55edmmjtvzXPSJGm7OXImSZKUEcOZJElSRjysKe0kLTn0Ce3s8KckaZs5ciZJkpQRR87UrrV0tEuSpNZiOJNamVd+SpKaYjjbRXirDLUp3opDkrbKc84kSZIy4siZpJ1vZ0yuLkntlCNnkiRJGTGcSZIkZcRwJkmSlBHPOZPaMG/LIUm7HkfOJEmSMmI4kyRJyojhTJIkKSOecyZlyDk/Jan9cuRMkiQpI46cSWqbnJ9T0i7KcCZJDUyZ8wIAw1+dutVtjv3YPoY/SSXjYU1JkqSMOHImCfCGtpKUC8OZpJ1uR682fWZT7aHFS0cdujPKkaQ2xXAm7eK8LYcktS2Gs7aguCpt+Kv+J6v2of5k/LkeRpXU/mQXziLiFOBmoCPwk5TS9WUuSVKZ7MioX92h0TpbHCLdyq04duYfQXVXfjZlhw7dejsRaZeUVTiLiI7A/wVGATXA/Ih4MKX0XHkrk6TNNBeMADhjp31cY0Fv8yDpBRvSriGrcAYcDbyYUnoZICLuBk4DDGeSstHiEb2Dmt+kqdG1hvdaG96yT/yglgTIHEbXdvLjyx6bAAAGYUlEQVQIYEtGLMELTpSv3MLZAcBrDZZrgGPKVMtfeehAAtrWxQWb30R23u3lr6EtafEh2Tb8+7HFNxyGJr9HS/pq+KtTmx7ZzLif2rq2GNYjpVTuGupFxBjg5JTSl4rlvwOOTild3GCbCcCEYvEw4PkSl9UTWFviz1Dj7Pvyse/Lw34vH/u+fNpT3380pdSruY1yGzmrAQ5ssNwHWNlwg5TSVKDV/hyNiAUppWGt9Xn6K/u+fOz78rDfy8e+Lx/7fku5Td80H+gXEQdHRBfgbODBMtckSZLUarIaOUspbYqIrwL/Se2tNO5IKS0rc1mSJEmtJqtwBpBS+jnw83LX0UDbPaO37bPvy8e+Lw/7vXzs+/Kx7zeT1QUBkiRJ7V1u55xJkiS1a4azJkTEKRHxfES8GBFXlrueXU1E3BERqyNiaYO2vSNiTkT8pnjeq2iPiPhB8bNYHBFDyld52xYRB0bE3IhYHhHLIuKSot2+L7GI6BYRv4qIRUXff6toPzgini36fmZxQRQR0bVYfrFY37ec9bd1EdExIv4nIh4qlu33VhARKyJiSURUR8SCos3fN00wnG1Fg6mkPgP0B74YEf3LW9UuZxpwymZtVwKPppT6AY8Wy1D7c+hXPCYAP26lGndFm4DLU0pHUHvj+YuKf9v2fen9GTghpVQBVAKnRMRw4HvAlKLv1wEXFNtfAKxLKX0cmFJsp+13CbC8wbL93npGppQqG9wyw983TTCcbV39VFIppfeAuqmktJOklJ4E3tys+TRgevF6OvC5Bu13plrPAHtGRO/WqXTXklJalVJaWLxeT+1/Vgdg35dc0YcbisXOxSMBJwCzivbN+77uZzILODEiopXK3aVERB/gfwE/KZYD+72c/H3TBMPZ1jU2ldQBZaqlPdkvpbQKakMEsG/R7s+jBIrDNUcCz2Lft4ri0Fo1sBqYA7wEvJVS2lRs0rB/6/u+WP824Ozm2+f7wDeBvxTL+2C/t5YE/FdEVBWz/IC/b5qU3a00MtLYX0le2lo+/jx2sojoDtwLfD2l9IcmBgbs+50opfQ+UBkRewL3A0c0tlnxbN/vBBExGlidUqqKiBF1zY1sar+XxvEppZURsS8wJyJ+3cS29j2OnDWl2amkVBKv1w1hF8+ri3Z/HjtRRHSmNpjNSCndVzTb960opfQW8Di15/3tGRF1fyw37N/6vi/W78GWpwKoeccDn42IFdSeonICtSNp9nsrSCmtLJ5XU/sHydH4+6ZJhrOtcyqp8ngQGFe8Hgc80KD93OJKnuHA23VD4to2xbkztwPLU0r/2mCVfV9iEdGrGDEjInYD/obac/7mAl8oNtu87+t+Jl8AHkvenHKbpZSuSin1SSn1pfZ3+WMppXOw30suInaPiB51r4GTgKX4+6ZJ3oS2CRFxKrV/XdVNJTW5zCXtUiLiLmAE0BN4HfgnYDZwD3AQ8CowJqX0ZhEobqH26s53gPEppQXlqLuti4hPAE8BS/jr+TdXU3vemX1fQhExmNqTnztS+8fxPSmlb0fEx6gd0dkb+B/gf6eU/hwR3YCfUnte4JvA2Smll8tT/a6hOKz5jZTSaPu99Io+vr9Y7AT8W0ppckTsg79vtspwJkmSlBEPa0qSJGXEcCZJkpQRw5kkSVJGDGeSJEkZMZxJkiRlxHAmqV2IiPcjojoilkbEv0fEh7ay3c/r7kUmSeXgrTQktQsRsSGl1L14PQOoangT3uL+SpFS+svW9iFJrcGRM0nt0VPAxyOib0Qsj4gfAQuBAyNiRUT0BIiIcyNicUQsioifFm29IuLeiJhfPI4v4/eQtAty4nNJ7UoxV+JngIeLpsOovQv5hcX6uu0GANdQO2nz2ojYu9j+ZmBKSumXEXEQ8J80Pnm5JG0Xw5mk9mK3iKguXj9F7fyi+wOvpJSeaWT7E4BZKaW1ACmluomv/wboXxfigA9HRI+U0vrSlS6pPTGcSWov/pRSqmzYUASsP25l+wAaOym3A3BsSulPO7c8SarlOWeS1LhHgTOLCZppcFjzv4Cv1m0UEZWNvFeStpvhTJIakVJaBkwGnoiIRUDdlZ1fA4YVFwo8B3ylXDVK2jV5Kw1JkqSMOHImSZKUEcOZJElSRgxnkiRJGTGcSZIkZcRwJkmSlBHDmSRJUkYMZ5IkSRkxnEmSJGXk/wMd0RzssMs0igAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['srch_saturday_night_bool'] == 0, 'price_usd']\n",
    "b = df.loc[df['srch_saturday_night_bool'] == 1, 'price_usd']\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(a, bins = 50, alpha=0.5, label='Search Non-Sat Night')\n",
    "plt.hist(b, bins = 50, alpha=0.5, label='Search Sat Night')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Count')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, the price is more stable and lower when searching Non-Saturday night. And the price goes up when searching Saturday night. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1599\n",
       "0    1449\n",
       "Name: srch_saturday_night_bool, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srch_saturday_night_bool'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>srch_booking_window</th>\n",
       "      <th>srch_saturday_night_bool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3945840</th>\n",
       "      <td>2012-11-01 02:48:30</td>\n",
       "      <td>84.000</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63387</th>\n",
       "      <td>2012-11-01 03:06:43</td>\n",
       "      <td>78.000</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3352426</th>\n",
       "      <td>2012-11-01 09:04:18</td>\n",
       "      <td>114.000</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5257418</th>\n",
       "      <td>2012-11-01 09:11:03</td>\n",
       "      <td>76.000</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7091061</th>\n",
       "      <td>2012-11-01 10:15:25</td>\n",
       "      <td>128.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date_time  price_usd  srch_booking_window  \\\n",
       "3945840 2012-11-01 02:48:30     84.000                   19   \n",
       "63387   2012-11-01 03:06:43     78.000                   16   \n",
       "3352426 2012-11-01 09:04:18    114.000                   56   \n",
       "5257418 2012-11-01 09:11:03     76.000                   56   \n",
       "7091061 2012-11-01 10:15:25    128.000                    0   \n",
       "\n",
       "         srch_saturday_night_bool  \n",
       "3945840                         0  \n",
       "63387                           1  \n",
       "3352426                         1  \n",
       "5257418                         1  \n",
       "7091061                         1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The useful features for our further analysis are \"price_usd\", \"srch_booking_window\" and \"srch_saturday_night_bool\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering-Based Anomaly Detection\n",
    "\n",
    "### k-means algorithm\n",
    "\n",
    "k-means is a widely used clustering algorithm. It creates 'k' similar clusters of data points. Data instances that fall outside of these groups could potentially be marked as anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take useful feature and standardize them\n",
    "# data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "# min_max_scaler = preprocessing.StandardScaler()\n",
    "# np_scaled = min_max_scaler.fit_transform(data)\n",
    "# data = pd.DataFrame(np_scaled)\n",
    "# reduce to 2 importants features\n",
    "# pca = PCA(n_components=3)\n",
    "# data = pca.fit_transform(data)\n",
    "# standardize these 2 new features\n",
    "# min_max_scaler = preprocessing.StandardScaler()\n",
    "# np_scaled = min_max_scaler.fit_transform(data)\n",
    "# data = pd.DataFrame(np_scaled)\n",
    "\n",
    "# n_cluster = range(1, 20)\n",
    "# kmeans = [KMeans(n_clusters=i).fit(data) for i in n_cluster]\n",
    "# scores = [kmeans[i].score(data) for i in range(len(kmeans))]\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(10,6))\n",
    "# ax.plot(n_cluster, scores)\n",
    "# plt.xlabel('Number of Clusters')\n",
    "# plt.ylabel('Score')\n",
    "# plt.title('Elbow Curve')\n",
    "# plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "n_cluster = range(1, 20)\n",
    "kmeans = [KMeans(n_clusters=i).fit(data) for i in n_cluster]\n",
    "scores = [kmeans[i].score(data) for i in range(len(kmeans))]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.plot(n_cluster, scores)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Elbow Curve')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above elbow curve, we see that the graph levels off after 10 clusters, implying that addition of more clusters do not explain much more of the variance in our relevant variable; in this case price_usd.\n",
    "\n",
    "we set n_clusters=10, and upon generating the k-means output use the data to plot the 3D clusters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "X = X.reset_index(drop=True)\n",
    "km = KMeans(n_clusters=10)\n",
    "km.fit(X)\n",
    "km.predict(X)\n",
    "labels = km.labels_\n",
    "#Plotting\n",
    "fig = plt.figure(1, figsize=(7,7))\n",
    "ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134)\n",
    "ax.scatter(X.iloc[:,0], X.iloc[:,1], X.iloc[:,2],\n",
    "          c=labels.astype(np.float), edgecolor=\"k\")\n",
    "ax.set_xlabel(\"price_usd\")\n",
    "ax.set_ylabel(\"srch_booking_window\")\n",
    "ax.set_zlabel(\"srch_saturday_night_bool\")\n",
    "plt.title(\"K Means\", fontsize=14);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pylab as pl\n",
    "\n",
    "# Y = df[['price_usd']]\n",
    "# X = df[['srch_booking_window']]\n",
    "# Nc = range(1, 20)\n",
    "# kmeans = [KMeans(n_clusters=i) for i in Nc]\n",
    "# score = [kmeans[i].fit(Y).score(Y) for i in range(len(kmeans))]\n",
    "# plt.figure(figsize=(10,6))\n",
    "# pl.plot(Nc,score)\n",
    "# pl.xlabel('Number of Clusters')\n",
    "# pl.ylabel('Score')\n",
    "# pl.title('Elbow Curve')\n",
    "# pl.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pca = PCA(n_components=1).fit(Y)\n",
    "# pca_d = pca.transform(Y)\n",
    "# pca_c = pca.transform(X)\n",
    "# kmeans=KMeans(n_clusters=7)\n",
    "# kmeansoutput=kmeans.fit(Y)\n",
    "# pl.figure('7 Cluster K-Means')\n",
    "# pl.figure(figsize=(10,6))\n",
    "# pl.scatter(pca_c[:, 0], pca_d[:, 0], c=kmeansoutput.labels_)\n",
    "# pl.xlabel('Search booking window')\n",
    "# pl.ylabel('Price USD')\n",
    "# pl.title('7 Cluster K-Means')\n",
    "# pl.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "X = data.values\n",
    "X_std = StandardScaler().fit_transform(X)\n",
    "#Calculating Eigenvecors and eigenvalues of Covariance matrix\n",
    "mean_vec = np.mean(X_std, axis=0)\n",
    "cov_mat = np.cov(X_std.T)\n",
    "eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
    "# Create a list of (eigenvalue, eigenvector) tuples\n",
    "eig_pairs = [ (np.abs(eig_vals[i]),eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
    "\n",
    "eig_pairs.sort(key = lambda x: x[0], reverse= True)\n",
    "\n",
    "# Calculation of Explained Variance from the eigenvalues\n",
    "tot = sum(eig_vals)\n",
    "var_exp = [(i/tot)*100 for i in sorted(eig_vals, reverse=True)] # Individual explained variance\n",
    "cum_var_exp = np.cumsum(var_exp) # Cumulative explained variance\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(len(var_exp)), var_exp, alpha=0.3, align='center', label='individual explained variance', color = 'g')\n",
    "plt.step(range(len(cum_var_exp)), cum_var_exp, where='mid',label='cumulative explained variance')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal components')\n",
    "plt.legend(loc='best')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the first component explains almost 50% of the variance. The second component explains over 30%. However, we've got to notice that almost none of the components are really negligible. The first 2 components contain over  80%  of the information. So, we will set n_components=2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take useful feature and standardize them\n",
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "X_std = StandardScaler().fit_transform(X)\n",
    "data = pd.DataFrame(X_std)\n",
    "# reduce to 2 important features\n",
    "pca = PCA(n_components=2)\n",
    "data = pca.fit_transform(data)\n",
    "# standardize these 2 new features\n",
    "scaler = StandardScaler()\n",
    "np_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(np_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate with different number of centroids to see the loss plot (elbow method)\n",
    "# n_cluster = range(1, 20)\n",
    "# kmeans = [KMeans(n_clusters=i).fit(data) for i in n_cluster]\n",
    "# scores = [kmeans[i].score(data) for i in range(len(kmeans))]\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(10,6))\n",
    "# ax.plot(n_cluster, scores)\n",
    "# plt.xlabel('Number of Clusters')\n",
    "# plt.ylabel('Score')\n",
    "# plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    661\n",
       "8    593\n",
       "7    429\n",
       "2    355\n",
       "0    354\n",
       "3    212\n",
       "6    150\n",
       "4    149\n",
       "5     79\n",
       "1     66\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = [KMeans(n_clusters=i).fit(data) for i in n_cluster]\n",
    "df['cluster'] = kmeans[9].predict(data)\n",
    "df.index = data.index\n",
    "df['principal_feature1'] = data[0]\n",
    "df['principal_feature2'] = data[1]\n",
    "df['cluster'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>srch_booking_window</th>\n",
       "      <th>srch_saturday_night_bool</th>\n",
       "      <th>cluster</th>\n",
       "      <th>principal_feature1</th>\n",
       "      <th>principal_feature2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-11-01 02:48:30</td>\n",
       "      <td>84.000</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.890</td>\n",
       "      <td>-0.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-11-01 03:06:43</td>\n",
       "      <td>78.000</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.231</td>\n",
       "      <td>-0.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-11-01 09:04:18</td>\n",
       "      <td>114.000</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-11-01 09:11:03</td>\n",
       "      <td>76.000</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-11-01 10:15:25</td>\n",
       "      <td>128.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.794</td>\n",
       "      <td>-0.659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date_time  price_usd  srch_booking_window  \\\n",
       "0 2012-11-01 02:48:30     84.000                   19   \n",
       "1 2012-11-01 03:06:43     78.000                   16   \n",
       "2 2012-11-01 09:04:18    114.000                   56   \n",
       "3 2012-11-01 09:11:03     76.000                   56   \n",
       "4 2012-11-01 10:15:25    128.000                    0   \n",
       "\n",
       "   srch_saturday_night_bool  cluster  principal_feature1  principal_feature2  \n",
       "0                         0        9              -0.890              -0.522  \n",
       "1                         1        7               0.231              -0.272  \n",
       "2                         1        0               0.567               0.547  \n",
       "3                         1        0               0.156               0.582  \n",
       "4                         1        8               0.794              -0.659  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering Approach\n",
    "\n",
    "The underline assumption in the clustering approach is that if we cluster the data, normal data will belong to clusters while anomalies will not belong to any clusters or belong to small clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the different clusters with the 2 main features\n",
    "# fig, ax = plt.subplots(figsize=(10,6))\n",
    "# colors = {0:'red', 1:'blue', 2:'green', 3:'pink', 4:'black', 5:'orange', 6:'cyan', 7:'yellow', 8:'brown', 9:'purple', 10:'white', 11: 'grey'}\n",
    "# ax.scatter(df['principal_feature1'], df['principal_feature2'], c=df[\"cluster\"].apply(lambda x: colors[x]))\n",
    "# plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "# return Series of distance between each point and its distance with the closest centroid\n",
    "def getDistanceByPoint(data, model):\n",
    "    distance = pd.Series()\n",
    "    for i in range(0,len(data)):\n",
    "        Xa = np.array(data.loc[i])\n",
    "        Xb = model.cluster_centers_[model.labels_[i]-1]\n",
    "        distance.set_value(i, np.linalg.norm(Xa-Xb))\n",
    "    return distance\n",
    "\n",
    "outliers_fraction = 0.01\n",
    "# get the distance between each point and its nearest centroid. The biggest distances are considered as anomaly\n",
    "distance = getDistanceByPoint(data, kmeans[9])\n",
    "number_of_outliers = int(outliers_fraction*len(distance))\n",
    "threshold = distance.nlargest(number_of_outliers).min()\n",
    "# anomaly1 contain the anomaly result of the above method Cluster (0:normal, 1:anomaly) \n",
    "df['anomaly1'] = (distance >= threshold).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "colors = {0:'blue', 1:'red'}\n",
    "ax.scatter(df['principal_feature1'], df['principal_feature2'], c=df[\"anomaly1\"].apply(lambda x: colors[x]))\n",
    "plt.xlabel('principal feature1')\n",
    "plt.ylabel('principal feature2')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3018\n",
       "1      30\n",
       "Name: anomaly1, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.anomaly1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAF3CAYAAADgjOwXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XeUFFX+NvDnDozAiEoQTITBtIALq+wI6hrWnAMKrusoYuJds6uuAcwru+q65p8BFVZxxICimFAEDAioBAUkK8MwguQkMMAM9/3jdtHVPdXdletW9/M5Z05PV1e4XV3hWzcKKSWIiIiISA9FUSeAiIiIiJIYnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFppGHUCfBi9913l6WlpVEng4iIiCinKVOmrJRStso1X6yDs9LSUkyePDnqZBARERHlJIRYZGc+FmsSERERaYTBGREREZFGGJwRERERaSTWdc6IiIgoGNu2bUN1dTVqamqiTkrsNG7cGG3atEFxcbGr5RmcERERUT3V1dXYZZddUFpaCiFE1MmJDSklVq1aherqanTo0MHVOlisSURERPXU1NSgZcuWDMwcEkKgZcuWnnIcGZwRERGRJQZm7njdbwzOiIiISEtCCNx888073j/yyCO49957Q01D3759MXz48FC3yeCMiIiItNSoUSO88847WLlypavla2trfU5RONgggIiIiLTUsGFD9OvXD4899hgGDhyY8tmiRYtw2WWXYcWKFWjVqhWGDBmCdu3aoW/fvmjRogWmTZuGbt26YZdddsHChQuxdOlSzJs3D48++igmTZqEjz/+GPvssw/ef/99FBcX4/7778f777+PzZs344gjjsDzzz8fWbEugzMiIiLK6sYbge+/93edBx8MPP547vmuueYadO3aFbfeemvK9GuvvRZ9+vTBJZdcgsGDB+P666/Hu+++CwCYN28ePvvsMzRo0AD33nsvfvrpJ4wbNw6zZs3C4YcfjrfffhsPP/wwevbsiQ8//BDnnHMOrr32Wtx9990AgIsvvhgffPABzjzzTH+/tE0s1iQiIiJt7brrrujTpw+efPLJlOkTJ07EhRdeCEAFU+PHj9/xWe/evdGgQYMd70899VQUFxejS5cuqKurwymnnAIA6NKlCyorKwEA48aNQ48ePdClSxeMHTsWP/74Y8DfLDPmnBEREYWtogIYMACoqgLatQMGDgTKy6NOVUZ2criCdOONN6Jbt2649NJLM85jLoLceeedUz5r1KgRAKCoqAjFxcU75i0qKkJtbS1qampw9dVXY/LkyWjbti3uvffeSDvfZc4ZERFRmCoqgH79gEWLACnVa79+ajpZatGiBc4//3y89NJLO6YdccQReP311wEAFRUVOPLII12v3wjEdt99d/z222+ht85Mx+CMiIgoTAMGAJs2oRLtsQbN1LRNm9R0yujmm29OabX55JNPYsiQIejatSuGDh2KJ554wvW6mzVrhiuvvBJdunTBOeecg0MPPdSPJLsmpJSRJsCLsrIyOXny5KiTQUREZF9RESAlBCR2xwqsQGs1XQhg+/Zo02Yye/ZsdOrUKepkxJbV/hNCTJFSluValjlnREREYWrXbse/K9HKcjoVNgZnREREYRo4ECgpSZ1WUqKmE4GtNYmIiMJltMq8KPG+fXvtW2tSuBicERERha28PBmcJfrZIjKwWJOIiIhIIwzOiIiIiDTC4IyIiIi0NWLECAghMGfOnMjS0LRp01C3x+CMiIiItDVs2DAceeSRO0YDKAQMzoiIiMi7igqgtFR1slta6stwVL/99hu+/vprvPTSSzuCs88//xx//vOf0atXL3Ts2BHl5eUwOtQfM2YMDjnkEHTp0gWXXXYZtmzZAgAoLS1F//79cfjhh6OsrAxTp07FySefjP322w/PPffcjm0df/zx6NatG7p06YL33nuvXnouvvjilOnl5eUYOXKk5++ZjsEZEREReRPQeKHvvvsuTjnlFBx44IFo0aIFpk6dCgCYNm0aHn/8ccyaNQs///wzvv76a9TU1KBv37544403MGPGDNTW1uLZZ5/dsa62bdti4sSJOOqoo9C3b18MHz4ckyZNwt133w0AaNy4MUaMGIGpU6di3LhxuPnmm5E+itIVV1yBIUOGAADWrVuHCRMm4LTTTvP0Ha0wOCMiIiJvEuOFpvBhvNBhw4bhggsuAABccMEFGDZsGACge/fuaNOmDYqKinDwwQejsrISc+fORYcOHXDggQcCAC655BJ8+eWXO9Z11llnAQC6dOmCHj16YJdddkGrVq3QuHFjrF27FlJK9O/fH127dsUJJ5yAX375BcuWLUtJzzHHHIMFCxZg+fLlGDZsGM477zw0bOh/r2Ts54yIiIi8qapyNt2GVatWYezYsZg5cyaEEKirq4MQAqeddhoaNWq0Y74GDRqgtra2Xi5XOmOZoqKilOWLiopQW1uLiooKrFixAlOmTEFxcTFKS0tRU1NTbz0XX3wxKioq8Prrr2Pw4MGuv182zDkjIiIibzKNC+phvNDhw4ejT58+WLRoESorK7F48WJ06NAB48ePt5y/Y8eOqKysxIIFCwAAQ4cOxTHHHGN7e+vWrUPr1q1RXFyMcePGYdGiRZbz9e3bF48//jgA4KCDDnL4rexhcEZERETeBDBe6LBhw9CzZ8+Uaeeddx5ee+01y/kbN26MIUOGoHfv3ujSpQuKiorwt7/9zfb2ysvLMXnyZJSVlaGiogIdO3a0nG+PPfZAp06dcOmll9r/Mg6JXNmAOisrK5OTJ0+OOhlERESOCaFedb0Nz549G506dbK/QEWFqmNWVaVyzPJ0vNBNmzahS5cumDp1KnbbbbeM81ntPyHEFCllWa5tBJpzJoSoFELMEEJ8L4SYnJjWQggxWggxP/HaPDFdCCGeFEIsEEJMF0J0CzJtRERE5KPycjVO6Pbt6jUPA7PPPvsMHTt2xHXXXZc1MPMqjAYBx0opV5re3w5gjJTyQSHE7Yn3twE4FcABib8eAJ5NvBIRERFF7oQTTkCVh0YOdkVR5+xsAC8n/n8ZwDmm6a9IZRKAZkKIvSJIHxEREVFkgg7OJIBPhRBThBD9EtP2kFIuBYDEa+vE9H0ALDYtW52YRkRERBGIc730KHndb0EXa/5JSrlECNEawGghRLZRS4XFtHrfLhHk9QOAdh6a6BIREVFmjRs3xqpVq9CyZUsIYXWLJitSSqxatQqNGzd2vY5AgzMp5ZLE63IhxAgA3QEsE0LsJaVcmii2XJ6YvRpAW9PibQAssVjnIACDANVaM8j0ExERFao2bdqguroaK1asiDopsdO4cWO0adPG9fKBBWdCiJ0BFEkpNyT+PwnA/QBGArgEwIOJV2ME0ZEArhVCvA7VEGCdUfxJRERE4SouLkaHDh2iTkZBCjLnbA8AIxJZoQ0BvCalHCWE+A7Am0KIywFUAeidmP8jAKcBWABgE4DgencjIiIi0lRgwZmU8mcAf7CYvgrA8RbTJYBrgkoPERERURxw+CYiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiIijTA4IyIiItIIgzMiIiKKVkUFUFoKFBWp14qKqFMUqYZRJ4CIiIgKWEUF0K8fsGmTer9okXoPAOXl0aUrQsw5IyIiougMGABs2oRZ6ITZ6Kimbdqkphco5pwRERFRdKqqAAAHYRYAQEKkTC9EzDkjIiKi6LRr52x6AWBwRkRERNEZOBAoKUmdVlKiphcoFmsSERFRdIxK/xcl3rdvrwKzAm0MADDnjMjSBx8UdHUHIqJwmQOxysqCDswA5pwRWTrzTKBFC2DVqqhTQkREhYY5Z0QZrF4ddQqIiKgQMTgjIiIi0giDMyIiIiKNMDgjIiIi0giDMyIiIiKNMDgjIiIi0giDMyIiIiKNMDgjIiIi0giDMyIiSlVRAZSWAkVF6rWiIuoUERWUwIMzIUQDIcQ0IcQHifcdhBDfCCHmCyHeEELslJjeKPF+QeLz0qDTRkREaSoqgH79gEWLACnVa79+DNCIQhRGztkNAGab3j8E4DEp5QEA1gC4PDH9cgBrpJT7A3gsMR8REYVpwABg0yZ8g+4QkJiNjsCmTWo6EYUi0OBMCNEGwOkAXky8FwCOAzA8McvLAM5J/H924j0Snx+fmJ+IiMJSVQUAeB0XAAA+xqkp04koeEHnnD0O4FYA2xPvWwJYK6WsTbyvBrBP4v99ACwGgMTn6xLzE0XuX/8CHn446lQQhaBdO2fTich3gQVnQogzACyXUk4xT7aYVdr4zLzefkKIyUKIyStWrPAhpUS5DRgA3HZb1KkgCsHAgUBJSeq0khI1nYhC0TDAdf8JwFlCiNMANAawK1ROWjMhRMNE7lgbAEsS81cDaAugWgjREMBuAFanr1RKOQjAIAAoKyurF7wReSV5VFEhKy8HAIirNgMbADRvDjw1aMd0IgpeYDlnUso7pJRtpJSlAC4AMFZKWQ5gHIBeidkuAfBe4v+RifdIfD5WSt4miYhCV14OXHEFAEAOuIuBGVHIoujn7DYANwkhFkDVKXspMf0lAC0T028CcHsEaSPNzZkDCAFMmxbcNvhIQKTOMyKKRpDFmjtIKT8H8Hni/58BdLeYpwZA7zDSQ/H1XiKf9fXXgUMOiTYtRIWADytE4eMIARS65ctVt0m64s2IiDlnRFFicEah22MP4M9/jjoVmTE4IyKiKDE4o0h8913UKSAiO/iwQhQ+BmdEaXgzIiKiKDE4IyIiItIIgzOKpSBzt5hzRsQGAURRYnBGscIbBlG4+LBCFD4GZ0RpeDMi4oMQUZQYnBGlYXBGkamoAEpLgaIi9VpREXWKeD4QRSCUEQKIiCiHigqgX79kD82LFqn3QCRjWzLnjCg6zDkjSsOcAorEgAHApk34FXtgJg5S0zZtUtMjxPOBKHwMzoiIdFBVBQAoRSW6YGa96WFjzhk5omGRfJwxOCNKw5wCikS7dgCALWhsOZ1IW4ki+c2LlmGjbJIskmeA5hqDM4olBlCUdwYOBEpKUqeVlKjpRDpLFMm3xWI0xUY1TYMi+ThjgwCKlTCKWhj4USSMSv8XJd63b68CswgaAxA5kih6X4XdLaeTcwzOiNIwOKPIlJcng7PKyihTsgPPB8qpXTtVlGk1nVxhsSYREdXDBgFkG4vkfcecM6I0zCkgSuL5QDmxSN53DM6IMmDOARUyHv/kiIZF8nHGYk2iNMwpIEri+UAUPgZnFEtB3jB4MyIioigxOKNYCbOohcU6REQUBQZnRGmYcxaeRx8FevaMOhVkhQ8nRNFhgwCiDHhzCt7NN0edAsqFDytE4WPOGVEa3oyI+HBCFCUGZ0QZ8OZERERRYHCWR776CpgzJ+pUxB9zzoiSeD4QhY91zvLI0Uer10K4mIbRlQZzzqiQ8fgnig5zzihW2JUGUbgK4WGPSDcMzojS8GZERERRYnBGlAFzzoiIKAoMzojSMOeMiA8nRFFicEZERBnxYYUofAzOiNLwZkTEnDOiKDE400RdHTBvXtSpiA92pUEUDj6sEIWPwZkm/vlP4He/A2bPjjolemNXGkTh4PFPYVq9OuoU6MVWcCaEaCWEaBV0YgrZ11+r1+rqaNNBzCkgMuP5QEEbMgRo2TLqVOglY3AmlHuFECsBzAEwTwixQghxd3jJKxxFiV9i+/Zo00FJzDmgQsbjn9xyeh/79NNg0hFn2XLObgTwJwCHSilbSimbA+gB4E9CiL+HkroCwuBMH8wpIB2MHx/t9nkekF2TJwMLFybfv/suUFNjf3kvx9q4ccCKFe6X11W24KwPgL9KKXfscinlzwAuSnxGPmJwpg82CCAdDBkSdQqI7Dn0UGDffZPvzzsPuOmm4LcrJXDcccDxxwe/rbBlC86KpZQr0ydKKVcAKA4uSYWJwVkwamuBRYvcLcvgjAoZj3/y4uef7c/rNufMuF/OnOlueZ1lC862uvyMXGBw5ozdk/mWW4DSUmDZMv/XTRQkXY5DXdJBlM44NovysN+Jhlk++4MQYr3FdAGgcUDpKVgMzuxx+jT/ySfqdfVqYI89gt0WUT7h8U9eOAnqveacFVRwJqVsEGZCCp1xIcz34KxhtscBTTCngCiJ5wPpyrhf5uODRLauNEqEEMWm978TQvxdCNEznKQVFiPyz/cLYV2d82WWLFGtgcKWjyc8kV08/smOa66xnh7GvSyfizWzfaVRAEoBQAixP4CJAPYFcK0Q4sHgk1ZYWKyZ2f77q9ZAbri5QOR7gEzkBM8HyuaZZ6ynh3Hc5HOxZrav1FxKOT/x/yUAhkkprwNwKoDTc61YCNFYCPGtEOIHIcSPQoj7EtM7CCG+EULMF0K8IYTYKTG9UeL9gsTnpZ6+WcwwOMts82bv68iUC7BhA7B2beo03oyIiMLjtc5ZPubyZgvOzLvrOACjAUBKuRWAnRBiC4DjpJR/AHAwgFOEEIcBeAjAY1LKAwCsAXB5Yv7LAayRUu4P4LHEfAWDwVk0WrUCmje3/iwfT3iKDz4kUJyxWNObbF9puhDikcRoAPsD+BQAhBDN7KxYKr8l3hYn/iRUoDc8Mf1lAOck/j878R6Jz48XonBujwzOnPHrxN+yJbh1E8VZ4Vx9KWpsrVlftq90JYCVUPXOTpJSbkpM7wzgETsrF0I0EEJ8D2A5VM7bTwDWSilrE7NUA9gn8f8+ABYDQOLzdQAKZihUBmf2OL1heAm0eHOisPHBgPKFH8eylMDQocC2bdaf53OxZrauNDYDqFfxX0o5AcAEOyuXUtYBODiR2zYCQCer2RKvVru33s8rhOgHoB8AtGvXzk4yYoHBWbCcnLy8QVJU3LRmDhrPB3LDj37O3nwT6NMH+Okn4N57My9XUDlnQogZQojppr8fhBBjhBB3CiEcdUIrpVwL4HMAhwFoJoQwgsI2AJYk/q8G0Dax7YYAdgOw2mJdg6SUZVLKslatWjlJhtYKpZ8zP1VXA127qq42guD2aWzdOjXOXHEx8OOP/qaJ8ptO538+5kZQvKxORACZRngp1GLNMwCcafo7C8AtAHYH8FSuFQshWhn104QQTQCcAGA2gHEAeiVmuwTAe4n/RybeI/H5WCkL55mtUPo589PzzwMzZgAvvujver3+Bp99BixcqMb1tHraI8qEOWeUL8LsSiMfHySyFWtaDRe9CMA0IcQ0G+veC8DLQogGUEHgm1LKD4QQswC8LoR4AMA0AC8l5n8JwFAhxAKoHLMLHHyP2GOxpnPGCZntIuCln7N8POFJb+bzP+qgiMc/6S6fizXdDqaTc1dIKacDOMRi+s8AultMrwHQ22V6Yo/BmXNB3zx4c6KwMeeM8gXH1vQmY3AmhOhmMbk5gIsAfBlYigoUgzP3sp3YbgIs3owoKubgjA8HFGd+tdbMpiCLNQH8N+29BLAKqmL/oKASVKjyNTj76ivgjDOAysrMnb26IWXw9fTy8YQnvelUrEkUllzHeqZrcUEWa0opjw0zIYUuiODsvvuAJk2AW2/1b51OPfAAsH498O23wMkne1+f+SQNus4ZUdh0KtbkwwnpriCLNSlcQQRnRkvBKIOzMNgJptz0c+bHzYk3OHJCx5xzPqxQVHI9rORzsWYexpvxxH7OnLOTc0YUJ8w5o3yxfTswcCCwYoX7ddx2m3rdsMH684Is1qRw5Xs/Z0F8ryBuHnvt5e1iQuSFTsGZwe9zd8IEoEEDoEcPf9dLehk/Xv1NnAh88EH2eTMdY5s3q9f1660/L/hiTSHEEVBjbO6YX0r5SkBpKkj52iAgyKfvIOqc/fpr/fUThUWnBgFBHf9/+pN6jfr7UTg2bgxu3QVdrCmEGAo10PmRAA5N/JUFnK6C4zY4W74cWLDA//TEgZOiYLcn74YNwMyZ7pYlckrHnDNdjBgBdOqU3Ef//a86r+fOjTZd5J3bQL3QizXLAHQupKGUouA2OGvbFti6Vd+n0KDSJWVqwLVwIdCokRrXslOn+vOvXQusWeOsOw8hgNNOU1nzRGHQMedcl2vLZZep83j9enUe33KLmv5//wc8+aR6kNqyBdh992jTScGaM0c1cnvrrfwu1rTzlWYC2DPohBQ6t8HZ1q3q1fzE/fXX9patq0uW6ceFVQ7YlClqoPF99gE6d7bOSezeHWjRwvn2vAZm+ZjdTsExn8e6BEVh2L5dnStCAH//e2r1AjvLAkD79kCrVsGkj4Ll5Fi/6irg/fdV3cWCLtaEGuh8lhDiEyHESOMv6IQVGq91zi68MPn/nDn2lunbFygpcbc9u8KoczZvXur06urk/15ucPl4wpPeCrVYc/r05P+PPw7065d53vRz2rhmrlnjf7pIX1KyWPPeoBNB3oOzN99M/m83qHj1VXfb0kXQXWkwOKOwmc//qI+/MLeffg47CVJ1LArOZvRooEMHoLg46pSEw+/jyLy+fC7WzBmcSSm/CCMhhS5fW2saguxKo5CKfyi/6VisGUY6vNzA43bNPOkk9brbbtGmIyyffx7MeqUs0GJNIcT4xOsGIcR6098GIUSGXkfIiaVL1R+Qv53QWj3l+L1unXPOdLnBUjzodP7H5Yan0z5zYt26qFMQDilz9x3p5Dppvu4XZLGmlPLIxOsu4SWnsOy9t3oNYxDvqJi/T22tv+vOFJzl2z6kwqFjnbMozicn29Rxn1Gqmhr/1mX1wB+XBwkn8jDejKd8L9YUwt/gTPeiFj/XQYVDp0BDt2M3U3ry9ZqZT3Jdr91czwu2WJPCle/BmZTAtm3e12M+CfN9n1Hh0WmEAIPuD0I8//PfyJHJ8TXNx0o+F2vm4VeKJz8DjSifIo45BjjoIOu0hFWsSRRXOuWcRcnJNYz7TH9+5JzdfHP9ZQq6tSYACCHaAzhASvmZEKIJgIZSygzjxJMb+ZIL9OWXmT/zI+fMip9ja5rlY1Y56U3HnLMozgMn53Tcr5lkj9GAwvxQXtDFmkKIKwEMB/B8YlIbAO8GmahCZA7OVq1KtuLU0Zo1wC+/OFtGSuacEeVSqF1peMHgrDAYx2GhFGvayTm7BkB3AN8AgJRyvhCidaCpKkDm4MwYG07Xi+K++6ox7uykz3wi+Z1zZvQIHlRrTTYIoLDpVESn6/UnHYMz/QVxLOV7saadr7RFSrnVeCOEaAggJqdtfMSpn7O1a+3PG1RXGjU1wP33198GkY6kBAYOBBYvTk57+23VW7yhtha4447k+9deA264IfqATZcGAWytmb/c9HMGFHixJoAvhBD9ATQRQpwI4C0A7webrMLjZz9nOh6oQvibc7ZpU/J/1jkj3c2eDdx5J9C7d3Jar17J3uIB4K23gClTUpd78knVUi0KUT70sJ8zSmdVQpLPxZp2vtLtAFYAmAHg/wH4CMCdQSaqEOVLg4BM/KpzZlXPTOcRAogAYGui7OGbb4AtW6znyRRk+F1X0ymr8+uTT4DVq8NPixW3wdn27WpM4ny95saJ2xECnBRr1tWp3zsuJS12grMmAAZLKXtLKXsBGJyYRj7K1+AsyDpnBj9OtqBO2DfeCGa9FC/mAKt/f+t5Mt1gonpIyHROrF8PnHIKcMYZ/m0r/Ttm+85+tdZ86SXgL38Bnn3W3fIEzJhhbz4/r69uizWfekr93q+84l9agmQnOBuD1GCsCYDPgklO4cqXfs6yCSoHwI8GAVbL6LofKX7MuTtVVdbzNGhgPT3q4zD93DAesubODT8tVtxeM5ctU686t4zXXdeu0W3babHmkiXq9ddfg0uTn+wEZ42llL8ZbxL/lwSXpMKUrzlnZkEN36Rzzhnlnw0bnBel2ZnfTs7ZihXA1Vcni0mDFOY5kR6AhtHPWZwaYZF1VxpOijXj1vWSneBsoxCim/FGCPFHAJuDS1JhKoQLRdyKNaPOsSA97borcNllzpax82BiJzi76SZVDPfWW8627yfdbm5egzPdvk8+0mFsTT8b3YXBTnB2I4C3hBBfCSG+AvAGgGuDTVbh8Ts4e/11+/OGdbC6yTmzc9Ix54zC5rTeil/BmR/Xh6efVuvM1SWOrueEXzlncbtZF5JMRf9AalDtpFgzbsF4zq8kpfwOQEcAVwG4GkAnKeWU7EuRU8YB41dw9vLL/qzHT37mnHkt1ly40LqnaTPmnJFf7BRrOqlz5uUGY1SAtzvKR/q20tPz669qmrnPtiCxWDN+nOac9eiReZ6CL9YUQhyXeD0XwJkADgRwAIAzE9MoAPnaz5mdrjQ++ww48EDVwWwmTrvSyPTZZ2lNWniBpiDZGZYp0w1m6tTk/2Ge25nSmT79m2/U69NPB5ueTNx2paHjdTJOggxy7FTad1qsmTfBGYBjEq9nWvz52Ig6f1VXZ+7TKBO/ggQnF54gD1YnXWlcdx0wf77K1XIiqNaaRH7xUqx5//3AzJmp0+wcr3ffDVRUALffrs5Dp0FMrm34Gdx4uV55vWbqdu5v2QIMGqT2SWVl6mfbtiVbHTq1ZIm/pRdDhvi3LrcjBBRksaaU8h4hRBGAj6WUl6b9OawOW5jatgUOPVRV3h0+3N4y+ZyDE1ZXGn6tI9sNY8sWNbSOMb4n5bdNm4BTTwVuvNH5sl9/DTz2WPL99OnAv/5Vf75sNxij2wcnQcw//wlcdBHw0EPqfXW1/WXN7OagBX3Ty3RzdZtzZuzv9evVg6F51JEoXXgh8P/+n/o/fcSIK68E9tkH2OywSV5NjVru8sv9SSOgRr3wi51jZ9MmNZ/xOznthDZuxdhZBz6XUm4XQlwL4M2Q0pN3ZswAzj9f/W/nAIyin7OwniS8PrWNHw989JH632udM683lormDsRmAAAgAElEQVQKNbTOli3Ac8853z7FyzPPAKNGqT+njjwy9f38+cCAAfXny1TnLCpxyWFIv2Z+950KRHv2zL6ccZ0cNEi9tm0L3Hqr/+lz6p13kv+n/wbvvadea2qAJg66gjdKcIzl3Vi/HujbF+jTBzjnnPBLZ0aNUgGm0S9dvhdrZg3OEkYLIW6BaqW50ZgopdRk8I784kdwpuvB5zXn7KijrKf7Mbam05wz42md4/oVhjB+52xP/2HnUtnZlpNe/XPxs1ize3fr+XJtU8dz2e/fWUoV/B11FNCqlbNl//53YMQI9Sel+9/73HNVoOUmpzK9w+CCbBBgchmAawB8CWBK4m9ykIkqZFHUOQuLDv2cCaEqWDvp9JIKz/77q7pehrvuCn6bTm4wYXB6Tvh5DoXRCW36/tbxmukX47tt2ACcd567obfWrfMnLSNGOC+WteK2K424sNOVRgeLv33DSFwh8qtYU5cGAWZhjRCQ6/u89JK9C3zcTmbyz08/Affck3wf1IOFmZ0bjEGHhwmrrg2i4Fdrzai/R5gqK4H33wcmTAhne0F0Qgs4K9bU4ZxxImexphCiMVT/ZkcCkAC+AvCclDJLhweFaeNGdaHYdVfny/rdz5nftm1TnVY6zQo32OlKw+n6rP7PNp/d9RmcDL5M5JWdOmc655yFxe/WmgYdg7Mgf4OzzvK2jaj3l9MGAYao022Xna/0CoCDADwF4GkAnQEMDTJRcbXnnsBuu3lbRxQXRDvb7NMHaN3aefqMCvyjRzvvVd2u9DTlCgL97NTT7w5CqXA5qXPmhtubkt06Z154ycXKFJzV1dXPVautTY5L6uSGbt5WpuuLsT0pw8lp9SLs48kIpPr39y89Unr7LXVn5yv9Tkp5uZRyXOKvH1SHtJTmt9+S/3vNpvUiiCcDJ8NBGczdTDz5pGpFFYbTTss9j506Z3F5wqL8kO0GM3Nm9lZ8dkRZh8wpJ7nhs2ZZt0Js1Ajo1Cl1WnGxmv799+4CwlNOUeuwsu++wC67qM54d9rJXkequfj1G/hxLfNaDPz558C//+09HYb584FLL1X/F2pwNk0IcZjxRgjRA8DXwSUpP0QZnAF6BBZB9WuWLts+a2hRcC9EtK3fyJu6OvUbGv135YtsN5ibblIVub2c16NHq+W9dvJsZz5jeDQ/h3TK1trunHPqT6urUzdwKxMn1t+XdrrRMH+fjh2Bk05Kvq+qUhXdjaHzFi/OvT6vZs0K76HXKyf3AzvH+Q8/5J7/hx9UIA7E7xpvJzjrAWCCEKJSCFEJYCKAY4QQM4QQ0wNNXYzFKecsqIM2yKbpduucWQVnudZn0CHApfqMi3wYLSjDFHQjnv/9T73abSlndxtW6R4/Xr3arcoQ9rm2bZv3bc6dax18GsfnjBnAapsdTk122P+B8dscdFCy6xA3y3sR9X3GfK/M9GBz8MHAIYekTovLdd3OreuUwFOhMSmBOXPqZ4/n4jbIyqd+znRo3JAp5yyd3bQ+/jiwaJF6aqZoGBdiNzmzixYBu+8O7Lyzv2nyg5OONMMUVEs7t+bOBfbay9s6amszF096ZTyUXn458PDD6v6Ry6GHqmtQruoWcQks0jk9hpy2vizIYk0p5aJsf2EkMkrPPQd07gx88UX9z4YNA/77X+CKK+o/jUZdrOmEk7Q6mdfvsfzc8JJzZuXvf1cBmiGuF8t8YFU0vWpV9mVKS1W9IR2FNfxRurffBl54wX56wgjGsm3jpJPU0GleuMk5u/PO5P/Z+vwyX/fmzrW/fqtrv3k/rFyp13BxQeacOV13+nibua4DcZCH8aa/jOzmBQvqf3bhhcAtt6h+s159NfUzt0+bUfRzFhS/+h+yYvdEt5tzxmLN+Mj02z/8sMoVq6rKvrxR5Gblww/Vn+7cBEiZjudevYB+/epPN4pBpQRGjsw8dFW282TrVjVUlbmxlNN1WPn4Y/vzmjsrNdTWOt/mwIHJ///zn8zzub3uZVtu5Ej33RgB/nS6HWQuntecM/P8jz6qrgPpdSt1KVGyy2a+QuGaOdPdcrmCrEwV0vOpQUBYdc6y8TvnLN9UVADHHgvsvXfUKfFu5Ej1ungx0K6du3W46Tk9TH7fEJ99NvPn5iD37LOTy2RLw3ffqf4eDW8mRmXeujV7QJPu00+BP/xBDRr/66+q/paZkx7mjz1WlXyYz/Ft27wVhWW7trltCJVtnV995W6dunB6ffVSrPnBB+q1stLZNnUTWM6ZEKKtEGKcEGK2EOJHIcQNiekthBCjhRDzE6/NE9OFEOJJIcQCIcR0IUS3oNLmxLffqle/s3AznYi69nPmhs51zuy01sz3TmjXrwcuugg48cSoUwIsWRJ1CuLFj+Nv3Djg6qudbyvbtrt3V8FQ+jw1Obost1rn9OmqQnfHjmo8RjMnwZlVlRSnOWdOOr71M+dMp640vK7TSfcoXnLO8kWQxZq1AG6WUnYCcBiAa4QQnQHcDmCMlPIAAGMS7wHgVAAHJP76AcjyTBc+pz9+rsAk0+e69nPmhg4DCdvNOXO733XZ124YT/g6BEa//z0wdKi9zjv9qKA+aZK9dIXJTrr9PN62bPFvXbm4TXdVlfV1xOvYjF6Ds2zXNh2DsyDW63T/+f1d3DYIiMs1O7DgTEq5VEo5NfH/BgCzAewD4GwAiZ5g8DIAo4easwG8IpVJAJoJITy2yQmP0zL9IIMzpzKl9aab6jeFD7JBgJNt2E2HVYssqzp5hVznLIjv+dNPwPHH565rZFizRo1CEVbfZYcfntpPUiFI/50z9daeztyZ6i+/qDq2gKp0fdxxalg3IPUcchr4OR2FwGsP/E4bBKSnb/jwzPMGUaxp1/jxKpdRh1ILL/xorfnII5nnj4NQGgQIIUoBHALgGwB7SCmXAiqAA9A6Mds+AMzd9lUnpqWvq58QYrIQYvKKFSuCTHbadp3NH3Vw5kez/MceAy65xH0anF5sgujnyc54hZnWp1twtn69v+sL8mJ1xx3A2LHJ4bvsWrrUevq2bcB112X+3I3ly70tv2qVysHx+rts3w5s2OBsGT8aBNgNcMy977dpo35bw7hxqt5iOqPjz0zbtiuooiuvOWeLsvRTEGWJQc+ewIgRuVsr6l59xs5v8+671vMb23F67dFN4MGZEKIpgLcB3CilzHYZs/o56v2cUspBUsoyKWVZKy/NVwIWZbGmLnT4LvkyQsCMGWrc1qE+jmprfOcgglDjBuVX/0OjRqlhcbLVkfL6PaxaZGez++5ASYn6XT75xP1277sP2HVXex2W6vbAANg7z3VIt/kcd9Na0y4/Swyi7M4kl6hba5oVZD9nXgghiqECswoppTEy3DKjuDLxajy/VgNoa1q8DQANasO4E4ecM8OFFwZ7g46S3Y4mdW8QMD0xFkem7gzcsBucLVmi+sJywjiOnV40jTQZrQKNXBpzLo9fNyzz966srF8M4sSXX7pf1hi31klOnp9daXhl/NbmLkieecbZOpwWa3rltVgzGz+HrdMhCMvE62+T7V7nd3DWuLH7dUclyNaaAsBLAGZLKR81fTQSgFFYdgmA90zT+yRabR4GYJ1R/BlHbltrhtkgwJjPnD3sJ1270hAC+Nvf3K3Pal1xl+s7HHOM6gvLST2fXMHZN99YDwyd/js8+GDq+uwWUzt10EHA88+7X97LcRBk55xu+d0xtdt0B5U7U1vr7MFBl7q2btcVREmB1wYB5nud15yzXPNv2aJ3oGslyH7O/gTgYgAzhBBGDYT+AB4E8KYQ4nIAVQB6Jz77CMBpABYA2ATg0gDT5ljQrTX97ucsKE4O8CC/i9/9nE2aBJxwQuo0nQKvIC4sdtfppr+gXMHZYYcBLVvaT5Nxw8sWnHn5vTZtcr+sX3Q63pycu34cm2GPOhBkzlmUJQY6HUPp0vdhWZlqMPTnP1vPb/StZ4f5OhO3ICyTIFtrjpdSCillVynlwYm/j6SUq6SUx0spD0i8rk7ML6WU10gp95NSdpFSOhwKNli5Dnq/Wmv6lSWuwzh9OhRr2h0h4I47gJ9/Dj49XunQX5EdxvG9datqPZbeWzfgbIgV47xo0MC/i6+UavzDCRP8WZ8VO/2IOfHii+rV2Adz5gC9e6v97Ccn525c6pyZ5QrOJk5Ux4a5mN0ut9dwJ3XOdOA1uP3hB9Wq+6WXrHPOjE6l7cjHOmccISAgbhsE+BGc2T1pgg6e/Kx7kc7vnDPdcyzjxji2Pv1UtR7btg044AA14PiZZ2ZeLtewZn4Wa27eDAwerMbIDUq2HvgNxndz09+cEVzecANw5JHZ5502zf56nZy7Vp28ptMhOLPTIGDOHNXp7fHHq+PjqadUow/dc85qaoDbb7ffcljnoA+w3wWPwUnXGzoci3YwOLMprK40/AhodDnxghz43Gs/Z+ms0prtN3/jDXvb90uUxZpupBdrGkOqAMADD2RP04IF9SvY+936E0gGelEH5j/9pF5vvz37fGbpNxo7v2W2wbrTPf20/XntyHX91KVY8/DDgdtuSx4TxmsQ1yYr6cf9yy+r/v9yGTQIeOIJf9JgV9Sd0JqxE1rKyK9iTa+dKwYtjCdIP08eq5yWl1+uP81pWo3cgric6FbsPkm6uai6fUqVEujWTeUImRm/j52cULvpNdLmdyOc0aPV+1tv9b5eu9utqVH9Embrf8uuX35RAYqf/GgQ4Oe5Vldnvb61a1UVB6MT3TBzwaRUjW/Mxo5Vr7m+e677hi4P7EH55BOgujrqVPiLwVlAoi7WDCtoqKxU25o6tf5nurbWtOq72GnOWT5wGkDZmW/GDDXfvHnqvZucLqsOWY3fZ8yYzF1OuP29/M45M4pJnQz07ZbxnSdNUiN6+BEQBvmAeNVV1tPDzjkzumrJxU3OmS6qqoAff7T+zOv3+fXX+h0N55Jtm17T88MPqoFBtnXF7TdkcBYQt11pBFlPKwhG30bGkC5mOtQ5M4aWySXo/b5ypcqR8Er3BgFGrmRVlXp1289ZOuN8WbQIKC1NTv/jH1Xl7WzL5uLXQ0RNjaqzZLeeoxfpgbURUPkxTqqb1rm5GOl87jl3ywGpv6+buofpx4edY9NNgwC3vAQvVp+3b6/GrA1Cx47O+z4Meh8uW2Zv+3F56GZwZlNYA5/b6WwzlzBzzrLRIefMamgZK1bBmZ/7sFUrNfSNW3a/75o19sc1TF/n6tXeB8NOv2n6HZylmzoVuP56Z9sw+H2OXHop0KmT90G5nfCzaNZw7LH+rcvgx/BNZnY7l862LjtpMo67uOW6BMG8D5zUX3S67qDpcG+0g8GZTUE3CDDmNwcJcbogWKVVh6407HIbnOl2ordoofprs3PspD9JtmypWlLW1bk/9tKDM7/2T7ZjyQgAo/wthAA+/1z97zXAtcNNgwCdOTleAWCnnYLfHhBusaaXbThdNuzjRafjc8sWVfzvdDzbsDE4C4jbBgHmm5BOB7TBydOnDjlndsUpkMxl/Hg1JJcbo0erYrl+/ZLTnOzr9ODMr2F8sv0+fufKmDnJkQqjODNdEDlnQfCjQYD593Kzr90Ua8a5zpmZDukPKw2LF2ff/uDBqj7o/feHkx63GJzZ5LQTWquLpdFkPtPngD/FmlGwSmuc6s/FpUGA3TQZ4zUatm9XFdXtBqFGZ6dOt50enDmtXG7noSWdnZvsm2+6+z3dBGdhHjfGd9c9OMvFzrXO3Gmxm33spUFAGLLtA6ctqnW7dwTdlYZZrjqTRofNfnfc7DcGZwGxOhD33z/5v53Wml4uDGHdIPIl5yzuN7dcXn5Z5aY9+WRyWhAVZL12EmvVzQngPefsscfcLXffffaONSGS9aDCuAnddx9w1FH1c850uykbdHvQKYTWmtnoVqSXqzK/XYccknueuDQMYHBmk991zuy01vRyQdDhwItTcGZFh31o8Pp9je4nli6tv85ff3VewXfhQtU6MV1QA5PnyjmbMyf53mpfWV387ezTBx4Arrkm93xAMucsjGNz2TJVfB2XOmc6dEJrPvaB4MbWdCvMOmdhy5VzZi5V8iJb9x7ZquRICcye7U8a/MLgLCBuW2uaeWmtGZSPPrKeHnaxppum5UEIO4DzWnfH6ol5+/bUXN1ctm0D9t0XKC+v/1lQY9xlC87Gj1etJMePzzyP1diedtkZgulf/0oGZ2HmwqbnnOn0QOEHP7+P+RiPW85Z1NvPJ1Y5Z6++CnTuDIwaFU2arDA4C4jbBgFO57HyzjvAW285X27dOuDuu7MHVT17pr4PoljTacstK7r2KeaXxx5LHah9+nQ1bmUmVv1MmffhypWZl03f10ZdDasLWRQ5Z+mi+N1qa1UHvEC4jUvyvUFAJn4EK2HnnG3dCtx5Z+bPhw93v23d65zpIFvOmTHu7KxZ4aUnFwZnNvnRIMDJ54D7E8ztuI833QT885/AyJHOl/WjKw0/L+A6B1ZerVmjfqvjjktO+8MfgE2bnK3Ha6etmzbV3886BGd2v9fpp7tLSy5R5JzpfnN2m9Md1Hn83XfAWWflns9I1003edve0KGqdeDAgZnnueEG++vTvesMHdNgNBSwyjnTsXoAg7OAuO3nzMk63Bo71vqiZ+SeOLnB+plz5uT75po37sHZokXqOxgDIZu/r3HsrF9vf312B3u3I9tyOgRnUYuyWNOg2xi9Ot30APWAY4eR7lde8batPn2yB2a56FBnz4u1a4FPP402DemlSVbBmU450BH0zBNPTm/2RjZpJuaDoKYGmDy5/jxBnXAvvGA93ch5KSlxng4/65zpEliFkY6qKrWdtm1TpxtB2fPPA0cf7T1dVsu4/X2sLmCbNgFz5zI4A6Ip1kw//3QLznLd9HQNLqTMfS3PxeuA3H5U84jaueeqEUh0kC3nTCfMOQvA3LnARRdln8d8sbr6amDIkOzz+CnTDTRTcJZNkF1pTJ+e+TNdLkZ2TurPPsv8Wfv2QLt29acbN9fKSutWkU6Z07lypbpQ+plzdvHFQLduwTXRd5LWqLsJ0CHnTLc+nHTKkXBi9Wp1XHvRtas/aclmzJjgt+FFrsAsqIZE2bBYM084qXM2YEDu9ZlvNt9+az1PUAdKphPBCM4aN/YnHV6Ds8suy/xZnLrSMAYBd+Lyy9XrhAmpjTD86Gm9Qwc1VJPdQeENxrBE6b+rlCqd5nn85uRYOuOMYNJgV5iBSKZOaPM9OAvrJrpxYzjbycbOdx07Nvh0BCnM4CxbzplODxEMzlzYuFH1f+RFkA0CcsmVc+bmRAl7bM04BWdmbtKdqXm3233w22/q9Ygj7M1vbKdVK/VqFZwZgrrI6nTRzCXMwCjOOWdTp6pRKwDnx3JYx0OcjrtsdMoRshJm+lismWfmzUtWaLznHuCuuzLP63RYkEzzR1Ws6eREyfZdvfZzli0ddtK4YoW37U+a5Gz+UaPU/pg7N3X6ww8n//cjYPWrzplTxvGR7TsEdZF1u9/OPTf8C6+X3MNRo4COHe3PH5cGAVa/3x//qEatEMJZ4xYgvKBJh6DGTRr23luPdOiw7kxYrJkn7roLOPlk9f/mze7XY/z45otLpgMiqpwzN+kIO+csFyGAM88MZzuffqrqhRldmHz9deo85mDN7T757jt3yxn8DFCy5ZwFddN0G+iPGOFvOuzwEpydfDJQWmp//nnz1Gumvuh0sX179uvIr79aT89UrKhbcKZbMOyHBQuA0aOBKVOC31YUOZTGNfHNN4EHH1T/6xScsbWmC15apG3Zoup0RVmsmavO2Zo19nuMD7JBgNecs0WLvG3fjunTgaeeAq680t7TV20t0KiR8+0884x6FQL44Yfk9Opqe8OOBBmczZqVvLkGcZG98UZg0CD/1xsUPxpw2GX89uk92esYnJ1/vr/r00m2khSvogoYDjggNQ1+jX8ZJCFy76/0blTuvjv5v07BGXPOXLAKzuyWXxsXbrvB2YYNqmgm05Oln4y0jR2bOtZZVA0C4sA40efOtR+ceVFZCRx7bHI7HTsCJ52Ue7kggzNzsW0QN80nnvB/nUHy2ijCzW+le87ZypWZe8B3I6xri91rn3m0jjD4GURIaa+7D6PRj87cVMkJI+ffDQZnLnjJOTOKRO1cXLZvV50fjhihepf2ix9DS9lZZ65A5P33na/TzmeAvScovxkne7Ybo9ebSno9uihak6X/ruZgQqeLW1TCzDkzpI+tqVsxW67h5OLeICCo/v0Af65j2dbxzDOqn8Vsg4bnIx3rmZkxOHMh14noZ85ZFAeQkwufl2LNXMOneC3WDJuxL8aNyzyP15wztzclP4sk0n9Xcx1MBmfegzM3OWdDh6a+1y3nzG+61TmLop8uv7z7rnr95JPM8zz9dDhpCZPVvVWn+0qMD6noeDkR990X+M9/9A7O7I7Tt2YNcMUVmeeJe1cadjhtjm1nn/Ttm/kztzclN0PHZNrH6d9h3brcyxSSKIo108UtONM158xOupo3B157Ldo0eGF0kn377Znnue66YNMQBavzTKeHSwZnLjS0aEbh5II6cKC91prm9Xo5QT/7LJiBo41K6oB1D9BBdqUxcaK3dftt0aLMw2KZ2dknL7+c+TO7x40f7AZnZjpd3KLidAB6Pxm/mdWIIzrTNai3czw77cw5Cm46ws53LNbMQ16KNQGguNj+TcyPp+gTTwQefdT+/HYGZQeA++5L/m81/lyQA5/rxm7L0FzBWa4BmcMMfp591np6+u9aXJz8n8GZd36c80bnrmFo2TK8bYUtyBwxL2bOdDb/wQcHk444Y7Fmnlmxwnv9goYN7XVCa86N8nrQLFhgf11uAkc/izV17K3ZL7n2yX77Zf88zODn2mutp6cHmObvpFNwNmNG1Clwx8vxbyxrtOgNgx/nq043RbOFC6NOAdCvn6oKY5brIY5yY3CWZ1q3zt3zvNOcs0wHRJcu/mW9OrmA+lVXLMh+znLZvDmcfnmc3pgy5ZzV1ak/nXLOMkn/Xc3vdek+RUpg8eKoUxGdFi2iTkF+8Nr5sx9eeaX+tKCCCJ2Ck6AxOMtDRq/cmdgJzoybWK5cuCiCs6eeSn3vdttu65wZ2/PynZ980v2yQcq0Txo2tK7LmE734EyH9AHAP/4BnH561Klwx4+cqDD7pMrnnG7duiQJ2j33RJ2C8LCfszyUK0cmV30Pc86Z3fprYQZnbta5eLEa6sOwcaPzXBRjfTNnqv+d1quIgtP96jVnybx8rmPiX//yti0rUtb/DuauQ3S5uOkanNvh5Vw1jomlS70t74TXMWzdbreQBRUQG/VMe/YMZv060f2hgsGZC16Ly6qr63camYnx+bvvAnvt5b6Zfq76YdnYnd/cU/1Vp/zsOhB55x13y5k1bux9HUHo1Qu4/nr3yzv57QYMUIHTL7+43166MWOiGfjcKTu5kJRq4sT4dcFRqII+z4y+z/KZ0UiOxZp5xCo4szP8hWHjxmRwZrdYc/VqNYSTTuObZQssF05Yirqf3Q1u6UcP67oGZwsW1C82dsJpVxrHHQe0aeN+e+nWrs1eXK1Lzpm5Y9y48aNBgBtHHGFvnNYgRDFAPRU2o8GdrsWafL50weqm2L+/s3XYDc7SiyfcthT1o6jE0Tq3b0ftjNkA2jvenh831rCCs7CzxnW4eGTLOdOlQYBuuncHvv3W3rxRFrdY9VcYhkLIqfHTe+8By5f7v17di/qCoFNumRlzziJi3GQ3bcpe7HTnnanv/RgmJKyDsW6zuzLYfM458yrq4EwIdkLrxt//HnUK7GGxZjwMHQpcfXXUqcg/OgVqzDmLiPkmZh7+Jhe3A+wG3SDASl2TpkCOXLBmzdTwJ5WVyWl+BGeNGnlfh46iDn569cr+edTp05WT89bLufr118A337hf3sm1iCgfsM4ZpXBb/FNU1k1ln5WWAhUVjpevqgJefNHZMpkO2KwHclERavfvlHPd69alBmaA92GfgMIIznS6kBgYnFkLKziTEjjsMPfLMzgrbDpeU4Jmvmbp1JCIwVlE3N7ERHUVJsnuarygfv3sL5e44H/xhbvtWsl2Iq/bpzPq9tjb1Xp32sllgkz8KP4NmptWlOab58aN/qUlkw8/dDZ/HLo/iYLbHO+wrV8fdQryl5eGQGEpxDpnZjfdFHUKkmJwC8tPboOzF3AlDsckDMd5jkZYDqJBQLbvMGNxc9e5g+axGt0K6wnQy371sxVlUM44w9n8338fTDrizsnDQpQ3SOacBScOAfqKFY5uK3nBfK/Q6aFeo6QUFrfBWX/8GwCwAPs7Ws6vzmzNcn0Ht8GZHzlnuhevbdgQdQooTHG4MQMMzoKk040/mz33jDoF4TLfE3XKOYzJ4ZJ/vAYPAu6irDCDM7d1x/yoLxZWzpnb7ey6q7/pIL0x54ziEqAX2oMjgzNK4UvOTkmJ7VnDLtYE3Oec+XER0z04o8ISVoMArxicBScuOWeFhsWalOLmm70tL5u1AAYN8icxAYmyQ9KwijUZnJEdcck5Y4MApW9foHNnf9cZxu+qU3ARF8w5I3/ddhtQXm579iByznKJMjgLK2jSvW4b6YE5Z/Hjd2toP/pvzCVfuxAKi07BbWBJEUIMFkIsF0LMNE1rIYQYLYSYn3htnpguhBBPCiEWCCGmCyG6BZWuQuWlQYDbQMeP/srcYrEm6cRJcNakSXDpyIXBmSKE6q3IT2G0gszXkVGCVIg5Z/8DcEratNsBjJFSHgBgTOI9AJwK4IDEXz8AzwaYrrzgNCgotJyzsHK0mHNGduj0RJ4NgzNFCP/7JQujX0LmnDlXcMGZlPJLAOnD6J4N4OXE/y8DOMc0/RWpTALQTAixV1BpK0TGQbdqlfNl4xicMUeLdMJizXgRArj2Wn/XefrpwLHH+rvOdDr1cB8LpaWQW5JjQOv0EBV2UvaQUi4FgMRr68T0fQAsNs1XnZhGPrvlFufLuM0dcvA5RQ4AACAASURBVBuc+RFYsViTdKLTRT+bbduiTkH+2m03YOzYYLehU85PLCxalJKlqdP+0+WSYbVLLG97Qoh+QojJQojJK1asCDhZ+opDsSbrnFGhyJVjEZecs6C0bRt1CpwJ4jcII0CPy0OATqQp/NDp3Av7p1xmFFcmXpcnplcDMJ++bQAssVqBlHKQlLJMSlnWqlWrQBOrc32iNWvC2xaLNaPfTqG47LKoU+BOriHHCv2mqdNNLyphdEJrtZ95jcquDskfRqfzNOykjARwSeL/SwC8Z5reJ9Fq8zAA64zizyht3Rp1CjJ75BFn8xdagwB2pRFPBx0UdQrcyXVRj0vv8BScMG78xnXefL1ncGbtOIwBAGxD8slKp4eIILvSGAZgIoDfCSGqhRCXA3gQwIlCiPkATky8B4CPAPwMYAGAFwBcHVS6nDDVE4y9QivWZCe08aTb/rzuOnvzpZ9f6d1h6PRE7rcjj8w9j26/axTCLNbMlZNLySEQa5Gsk6BTcBZY2w4p5V8zfHS8xbwSwDVBpcUtnXPOnPJy0IXdIGD0aHfLmbFYM55025+NGwOtWwPLl2efL/3Gm37O5HOds48/BnbZJfs8ixdn/1w3QfwGYeaeFhcn71+6nVO6EJCQ7dqjtqrAcs7yQT7lnHnh9uR2020HAGze7G45MwZnuV157PyokxALdvqOSg/O9t479X0+F2s2bRp1CvwX1wYBBnPOWZyvUUH6DCeiqKoy6mRkxF5RstA9OKuqsj9vFDlnUWKxZm6tp4yC6vdZH7rtTyntB2eTJgE77wx8/71qvfnXv6Z+no/GjYs6BcGIa3BmnD8MzuIvTy8Z/tA9OGvf3vkyBx7ofJk4ntzMObPBwyjXuYqx3NJtf0oJ7LRT7vmEAHr0AH7/e+Cii1SQZpaPOWclJcCf/xx1KuIj7GJNg27nFNnD4CyLzz+POgX+MZ4EzzjD+bJxPLnvuSfqFOhP7OY+wvrLX3xMiMbsBmfpuSLpFbKd5JqEVe/lkUeAkSPdL9+rl39pcctOYwRdhJlzZu53L47Xb2JwllV1ddQp8J+bC38cT+6amnC2E8ci3x3+7H4smWeeAe6/38e0JOh4rLmpc5YenOmYc9a7N3Dmme6X1+G3+uc/g1lvXIs1Dcw5iz8GZ1nk01AmXi4MPLkzi/O+ueKJLq6XLS4G9tzTx8Qk6LY/nRRrmqWPGKBjzpnXfa3DbxWnLiPCCM4WLlSvDM7ij8FZFvkUnBVag4CwxPXCN3iwuzqLADBhgnoN4runr/PKK4Hycv+344SbnLOuXVPf65hz5pUO14WgBvrOh640DHG9RhU6BmdZ9OwZdQr89/PPzpfhyZ1ZXPeNl6f4AwJs4Jm+P08/PdqWjiUl7uqcNW+e/XMd5EPOWVDBWRCiKtbUIYgm5zS8ZOjjmGP8X2dULUCNJ8ERI5wvO3Cgv2nJJ3Gtl+jlgh3kTcbqhh/VMGA77wwMGOCuWDNdnHPOjjjCenrUwVnDhsEVAQdRXBpVcBblMHrkHoOzkMWpjoRh8OCoU0B+++knZ/Pfemvy/7BzgaK4ufzpT8CGDWoYJjvFmn4GZ7rVORs7Fti0qf70qHNkdt45uuDs0UedrzOqYk0GZ/HE4CxkUQ0PodOwFBS99GK3XMxdk1gNruwXq4AhijFad989+f38CM6KioCbbvKeLj/ZDc4aNao/VqiT5f1mFGU2a2a93/04LnMFZ24essO8BpvTF+UYx+QegzOiAjNpEnD99er/H34ADjkk9zLmG0uYxZpbtth/8t9vP/fbbdYs9b05ILNTrPnYY9k/1zHnzKuogjPj98j0gOFHunIFX2F/99JSZ/Mz5yz+GJwViLhc8Cl4PXokL95duwJTpwKdO9tfPszgbN48+zeX11/3b7vmm5udnLOTTsr+uZN9dskl9uf1QkoAFRWulzcXa/70E/Ddd97TZIc5OIuqWNN8vHzxBfDmm8Gkw+D0e0YdnBm/0YABqdNLSsJPS1wxOCsQQkRfgZf0dfLJ1tOtApMgA/30Y/SII+zfXLwc3+nLmnPS0nPV3GjQwLpo0Mphh3nfnp1iaylR/+4J4DiMsbUN8/fZd1+grMxm4jwybvx+/C6ZGMG0ndy5o48Ops8/q/TYNXNm8v877/Q3LXY0baper78+dV/lOgcyncO5zu3t24Fzz7WfvjhgcBYCI1eidevo0iAEsHVr7vns5BLExVFHRZ2C+MjUJcFll6lX883B+D/oOmd1dcBxx/lTZybXxf3xx9Wrccz8+9/Jz/r3T97gevdW6zJaMPfurZZNH0szXVFR6nqsmM89r92V2K4TVVVVb5JA7ij33nuBJ5+sP13K4HrtN7jJORuTFm926GBvuRdfVK+nnJI6Pf14cnIuXHFF6vtp05LbycTpubZoUfL/l192tqwfdt1VvaY3JClZ+4vjdY0dm3seIYBbbsk+z9VXO950pBicheDww9VrEDczO/VhDHaCM6/efz/5/w032FvGOJH91qOHs/nbtAkmHUB4RVVuZbqZP/UUsGZNauBgBGd+B/J9+yYfZM47L7mdMIplLr1U3XC//FK9mgd2LylRAce6dcBrr6lpxrm83372jvOiouR6Tj019bNRo5LzGG68sf46unWz/32WL68/bcwY4Lffku+lBNCunf2VJmzcqBqItGhh/XnQVSiMMT2d5Jwdd1zqe7v9PRpBWJMm6ve/5prk9JdeAt56S703f+dc/WOm/44HHwxcfnn2ZXSulrL//vWnjRqljmGjo+u7zpmBSY2OQZO63+rPDHVu/Pe/1uu3UycWALp3B667LvPnZWXqPDecf7563X13e+sPG4OzEFz3kboapz9ttWrlfd12Dywh7PWx5jWXwvwdrXJjrHqlz/XUaFf6DU0IdfG028N8epBoUeLjWqZiiSD60nMjU85Zgwb1b4LGd/G7A9DHH1eBy5/+lNq3Xt++yZyOZs1UDhQA3H03cNZZwFVXqRtlJpnOs9NOU7kKuW6Mhl13df+dzTfXt98GKiuT760G777qKmD27NTcKa8DjR9xhEUOn1UnhkXZWy9EXW/I2JdB1jmz2uauuwK33aaKnS++WOUqG7+JOR177eX/9jt1Sv6vW4mAudrismXAqlXA736nGskY++X+aWeix5Yv0QSbLddx6qmZWzPbfXhv0ECdLyeeaP25+Rp8+eXJINqP+3AQGJy54LT8v+HSRNFBTeqB6Uc9BScXJzvBmTmXws3NwBzcWeVEWfWN5FcOzAknpL4XQn2HV1+1t7zxJGV44IH68+y/v/NuKIDMx4yTnE9AfZcZM5xvPxcnXQNYHXPpwxW5tcsuwPjx6uJuuOiiZGXzoiIVT0gJ3Hcf8N57ahB2o/jVivE0nV6v7sMPgT593D0c/OUv6rjNliPasaPKFUlvRdqkiXpIeeQR9XmTJupceeGF5DxCqOWvu04FBIYLLqhfLGYl/biSEmjcOHVa27awfHL5xy3SMgC7/XYV0Oby178m/3/++WTJgV/WrlWvmYIzu7+nEZSfeiowf37qdeiCC9Rr+gN127bAxIn1H4q7mIap3bAhua777qu/XWOdRUX2i6+N1tVHHaVydw1vI/qKVkaJTOPGquqOZY5qovh8AOz1aG5+CHJ6v81UhUGIZI5yly7JFtQMzvKI05tjQ6iIRf620dd0NGliPzizW+fM7MILnafJHJxZZTFbBWdOAxS7nD5Vt2tXP6A0d74KqNwHc0nQvvvaW3emC4bT737ggcDvf+9sGTvMwdl556nXTI0EjIulORfms89UEAWkPuWna4tkHadumJLyWbZ6YcZDg5uWosZ6X38dGDkSmDtXdSfixb77AjU1KoDKZPZsVZ9owQLrz2++WX1eVAQsXpw5h9d4SDr9dGDYsNQgLhNzIPbVV7nnMet++3HYaHGp+ve/VUCbi/mc6NcvORarX9asUa+Z+jmzmxP64ovq2PjoI/XQVVOT/MwIqI1jJ9e1xFwMXl6u1iWlyn3v1g0YNCj5ubHOv/1NtUY2HHpo8v/TT09d/7Jl6nXvvVOnn4vkkC/v7pT2dOmROeDMZutW9Z02W2eKKYmL5uGYCADYG6l1z8zH1RdfOO8k2yxbcGY0lOjSJXlNYXCWR5x0OwAAjaHO+trtqbvba5a8k1Y4dos1zdz0aG20xrnzTuvlrYIzu63YnHJ6Ixei/jIPPZT6vrQ0Nci120IwU70pp8FZUC1uzU+qRrFhprpUxnF7xhnq9cgj1QXurrvU+/vuUxXGrVQhWa5dhwbYp8HSHe+zfTfj5nfVVZnnyeTYY9Vrs2bAmWeqANdpfcQolZWpfeMkd9J8c7cqNrUSxfifTz3lbP6//c065yzbA4EfnFyrzUFWgwbAlCnAlVcmpxnDYaUHYN9+q1579AA++CA1x994ILv44rSNJeqJ9Gv6Gs4efLb9RGZhXAvSHxbM9bXMrOqc1TNwIFBSguZQkXU/DEr52BwYH3108gG4e3cb606TLTgzGkp06aKKXwGVC+pmO0FjcBYCIzjbIlIfVb0U53XvDtxxh7Ocsw0bnG3DKri6887s9dIaNlSf33+/9edWwZn5qTOXOXPqX4i3brVOk91907Ztcn47Pb2bt2V3CBu/gjO7nNYdNC7I112nnvS3batfcT2dsS+MYpYDD1TL9e5tPf+lDV5JeV8nilE1ZCx22029zxacNWmitmVVTGRl61a1z7dt06+OThiMgNQJ4zwMq8udhx5KVrC3UleX2mp26VJVhG0EZ+a6kE7S7GSsYDf7IlOOpOGQQ9RxaVVEXFeXzGksL1cP1LW1KjDftq1+QIfKStTWAs+tv9Ay6/WFF4BPPqm/nWuuUeuzuk4Y14L0755er3PsWLUOW21KysuBQYOwc/tWqEVD3N0udxPSujpVhOyXoqJkf4StWgErViT/nzhRv856GZzZtHJl6nsnT1KZgjOjLxgA2FlsxED0z7iO9HobjRvbCybM7D5BZ9OiRfYctbo69XmmdFkFM3vsYX/7xcX1n/CLi63TZGfffPxxsl+pLVtyL3PQQeqC5FSmE9+c7muuAba3K93xfk8srb+ATVb74+23VYuzbPMb6bRb8T39tzaWO+gg9Wru+6ru8COB9u1xLZ5W7/dqg6KLy3dsO9eNMNtxBaQWTxjHid+NFnRh1ZrTzM1oCcbDYpCtuvfZJ3V72X7PoqLU4M34/Y1izebNk/Wbjj7afhr6Z77M1mO3WNPMzkN3puOyqCj1+rbTTslzM1ujnUzpu+wy6w6SGzRQ67O6Thi5WLm6C3F8fpWXA5WVaCBrIRZVpnxk1KlLX3+m3Fyr1siGTGNBC6HqqBo5ZkYjox49sm8rKpolR18tW6a+d9L8tlE7VfO/Lq1Y0xyc7VTSEEc0mppxHekHjtMix/79nV90rZ6qcrWcyZVjkx6cvfNO6gU7F/N++OorYOHC5Pv0YlvzxeSvf02t9wGowOGUU5LB2bRpubffq5f1fkzvByldpuDMSGPr1sDTTwPiX8nH+gZILnTovurpwCqASe+qyugm4NdfVW7D4sXqKffcczP/fn/5i3qiv/nmzN+hslLVB7GjVy/g+++Bb75JVmivbbcvUFmJ6+ddq97vrLLMjH3gNcfG6RA3cfbYYyoX2VzPp7pa3Xzeflt1HbFkSbKuUjqroiijqDfIgNbcLYKdIGaXXZIBmHHum4OzPfcEZs1SxaNBVI8wGm0deKD9ZcIY4PzBB62L+P/5T9Xq8d13VVBm7LNnn829TqPY/OOP1cNVphzwIDgpRdprr9QHsc8/T/3c3COAuS6tECpTwzie+vRR1yijeoZ2pJSx/fvjH/8og6ZuGfX/32OP5Ptcf3V11uu54ILk/82bSznhnlEZ13H00anvTzhBratdO/vpcPI3ZIiU//tf/elvvZX6HdL/3n7btPNefbXe582bp76fPj11fYcemj1dP/0k5e9/n7qsYfDg1Hnvuivz7wlIaRw+S5dK2ayZlBMnqvQDUp52WnIZIdS0Fi3U+9atk+u48cZkWpo1U//fcUf9dJ90kno1LwtIedFFyenpaWyDKrl3g6Xy8Yu+kx9+KGWDBlKuW6fm6dpVzfPss+p9WZl6f8MNdo7qcC1ZotI2YYJ6v3mzlE2aJI+loUOlbNpUyq1bvW/LfJ4VCqff+bvvpFyxIvl+zBgp33tPyg0bpPzqq+T0ykopZ81yl6Z586RcsKB+Gtevl/K446Ts0UPKlSvVZwsXqu3Mm5d6bhiM82rVqtR1bd6cus0lS6T8/nv1/w03SFlaKuXo0clps2ZJ+fPP2dNttS9Hj5Zy27bc3zkOx97jj6s0Xn99clqmdG/bVv/3ML9fvdpbWmbOlPLqq9W6brst9/zGdpcty55uKdVvXVmpjonf/U7N9/rr3tLrFwCTpcwd3+ScQee/MIKzu+6qf2BKKeWee2YPIqwO6kaNUtczYEDy/5YtpZwxI/M6Tjwx9f3556t1detmPx1O/qSU8oUX6k//5JPU75D+98YbiR336qtSlpTU+7xZSU3K+ylTUtfXq1f2dM2dmzk4e/TR1Hnvvrv+79mwYfJzI8DNZcIENX/Tpuq9OcB84onkfFVVUo4aJeXHH6vPzIHmySerfbN2bWoajeBsjz2S69kRnLWxlz4p1Y31tdfsz5+vACmvvDLqVISrdWspzz036lRkZzdwMZ9bhocfVu+3bFHvzdfkqNLp97JhmTlTpfGLL5LTrr8+c7qfeEJ91ru3eu/3d3zgAbW+/v1zz5u+7f797aXlgw/UfAsXuk6mr+wGZyzWzOH++9Uhkc5O+fTZZyc76HvlFeCHH9T/U6cCw4eryvV77aWK4h98UGUld+miisiMlnKdOqmm4cOHq7TceaeqFP388+rz998Hnun7rfcvaqFPn/otU20Xaw4YUH/sDgDbN6eWPabX38pVwT7b5+n1qazqYcyfr4pW7rjD/pjPRpa7UWyaqQ+etm1V9xNGEWbbtqrbBkDVczj/fOyo/A6oYm0jjeZiTyOb3kljgaZNU/uXKlRS1i++znfLlqmizHywenXy0cXwj3+o98b5kOma7IeRI913s3L88f6mJQgHHaT2nbmO3hNPZN6fxtiYQQ3s7qWLHKO/w1xOP13NF7tqD3YiOF3/wsg5MwOkPOYY9b+RHWvkjrRqJeX++6fminz7bQiJevVVKYuLLXOZbsWDO/5vivWyA35K+fxrHJ6ai4VD6j2dPP106jqXLFHT04vnjL/vvkssmCgLTP+8KdanvK+sTO5bQMqePa3Xa/xt354552zECDX9uuvU6/vv+7OLKyvV+k49Vb3/8Ucp//EPVXSSXrQipSoOuOkmlf2+fbvKwZs9O/n5++9L2aWLyin95BMpzz5byoceSn5uLDN/vj/pJ4paHHKVvKqtTebu5Su/f8e771bru+ee8LcdFbBY03/LliVvxtu2STltmnq/apUqVvrtN1WPYsuWkAIzKaVs394yCELi5zW/b45VO/6fj/3qfS4BeSselB+1vmTH6teuVVnaS5ZIWV2d3OyGDep7G8tu3Kj2R3q6SvBbyjZOazJmx/+LFydnN6adc07mwOymm9S8mYKz7dtVPZft21URo59+/FHVlyEi5/Llxlro/P4djao9998f/rajYjc4Y7GmA61bJ/uwadhQDb1itP5o2lT1mN6ypcp+N3dEGKhEU71hUOON7I/5GIK+OBlqNOWX0QetsBz7YQF2KdqInuJd/B4z0BaLAQC98FbK6h7a6W6c+mhycLLddlNZ2nvtldqqsmlT9b2NIr+SErU/dkh0Ong0kmONzGxchjefSraBthreyTxsj7GvjVejPx2jqNXc2hVQRYT77adejb7L/NK5s7P+2IiIKLsrrlDFjX37Rp0S/QgVyMVTWVmZnDx5ctTJiFZpabLb40zat0+OtFxRoeqDVVWp6KqmBmLjbwAA2XJ3VQHB7kjhAH75RTXZtwxGKyqw/o5/Y9jiI3HUXgvQ+T+XAuXlmDdP1TUwdyZbVQX8738qaf/7nxoWp3Nn4D//UQNcjx6t6g4UFQEbN6pOA9PH0iQiPc2fr7qgMfq/o3jyq9sbNy6/HJg+PTnOblwJIaZIKctyzsfgLOYqKtS4Gpl6Ri0pUTWkswRcs2erACsOFVqJiCgaUQZn+cJucMZizbgrLweGDEntJddo+tK+fc7ADFA5WAzMiIiI9JCng5sUmPJyR0WRREREpC/mnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFphMEZERERkUYYnBERERFpRKvgTAhxihBirhBigRDi9qjTQ0RERBQ2bYIzIUQDAP8H4FQAnQH8VQjROdpUEREREYVLm+AMQHcAC6SUP0sptwJ4HcDZEaeJiIiIKFQ6BWf7AFhsel+dmEZERERUMBpGnQATYTFN1ptJiH4A+gFAu3btgk4TERERAXjvPWD58qhTURh0Cs6qAbQ1vW8DYEn6TFLKQQAGAUBZWVm94I2IiIj8d9ZZUaegcOhUrPkdgAOEEB2EEDsBuADAyIjTRERERBQqbXLOpJS1QohrAXwCoAGAwVLKHyNOFhEREVGotAnOAEBK+RGAj6JOBxEREVFUdCrWJCIiIip4DM6IiIiINMLgjIiIiEgjDM6IiIiINMLgjIiIiEgjDM6IiIiINMLgjIiIiEgjDM6IiIiINMLgjIiIiEgjDM6IiIiINCKklFGnwTUhxAoAi6JOB2W1O4CVUSeCAsXfOP/xNy4M/J2D115K2SrXTLEOzkh/QojJUsqyqNNBweFvnP/4GxcG/s76YLEmERERkUYYnBERERFphMEZBW1Q1AmgwPE3zn/8jQsDf2dNsM4ZERERkUaYc0ZERESkEQZnZIsQYrAQYrkQYmaGz88WQkwXQnz//9u7/1ivqzqO488Xv0tI/JHNSYxECYgQLawwEZmy1o+FSyqlX9NF1NSy4aytVq2clJtaMwGrda0VBP5I1BGwBpqGdRXuxQGWCeVIkkbDvPIjubz745zv+Hi5v77fe78fvszXY7u79/v5nO/5nM89O+f7/pzP53uOpCclvb+wrz1vb5G0srC9SdKOwr4pZZyLda2P9Txa0hpJ2yRtlTQmb3+bpD9JelbSbyQNKedsrDN1qmO35QZTaz1LurhQjy2SDkianfe5LZfEtzWtVyRNB9qAX0TEpE72DwdeiYiQNBlYHhHj8762iBjeyXuagIci4p76lt56q4/1vB64KSLW5nSHI2KfpOXAfRGxTNJioDUiFpV2UvYadarjJtyWG0pf6rmQ5mTgb8Aot+VyeeTMeiUiHgX+083+tjgS6Z8AOOo/DtVaz5ImAoMiYm0h3T5JAmYClQ/tu4HZ9Sq/9ay/67je5bXa9FOffTmwym25fA7OrN9IukzSM8DDwFWFXcPysPkTleHxgpvy0PptkoaWV1qrVRf1PA7YK+k+SZsk3SJpIHAKsDciDuV0O4Ezyi+1VaPKOq5wWz7OdNNnV3wSWJr/dlsukYMz6zcRcX8eFp8NfLewa3SedfpK4HZJY/P2rwPjganAycCNZZbXatNFPQ8CLgQWkOrzTOBzgDrLooRiWh9UWcfgtnxc6qbPRtLpwDuB1ZVNnWVR3xK+fjk4s36Xh9PHSjo1v34h/94OrAfOza93RXIQ+Dlw/rEpsdWiQz3vBDZFxPZ8Zf1b4DzSOn0jJQ3KbxsFvHBMCmxV62Uduy0f5zr22dnHgfsj4tX82m25RA7OrF9IOis/k4Ck84AhwB5JJ1VuceSGfwGwNb8+Pf8W6cqt028VWePoqp6BZuAkSZUFfWcCW/MzLetIz64AfBZ4oNxSWzWqreOczm35ONNNPVdcwZFbmrgtl2tQz0nMQNJSYAZwqqSdwLeAwQARsRj4GPAZSa8C+4FP5G8BTQCWSDpMuhhYGBFbc7a/yh29gBZgfpnnZEertZ6BdkkLgN/nDv8p4Cc52xuBZZK+B2wCflbiKVkHdapjt+UG04d6RmmKlLcCj3TI1m25JJ5Kw8zMzKyB+LammZmZWQNxcGZmZmbWQBycmZmZmTUQB2dmZmZmDcTBmZmZmRk9LxjfIe10SRslHZJ0eYd9P5C0RdI2ST+qTFvSWw7OzKzuJLVLasmdVaukr0rqtv+RNEbSlVUc45R8jBZJ/5L0z8LrIZL+2PczOeqYMyQ91EOakZK+1N/HNrO6aAI+0Mu0z5NWyfh1caOkaaQ5PScDk0grZ1xUTSEcnJlZGfZHxJSIeAdwKfBB0rxL3RlDWvKrVyJiTz7GFGAxcFvldUT8LyKm1Vr4PhoJ1D04K8zcbmY16mzBeEljJf1O0lOS/iBpfE7794jYDBzumA0wjDSx71DS/HIvVlMOB2dmVqqI2A3MA65RMiZ3eBvzTyWIWghcmEe+rpc0MC+23ZwX2P5CNceV1JZ/z5D0iKTlkv4qaaGkuZL+LOnpytqvkt4s6d58vGZJF/SQ/7fzLZH1krZLuq5wHmPzedyS095QOI/vFPL4pqRnJK2VtDRP+trlh4OkJkm3SloHfL+a/4eZ9dpdwLUR8S7S2rJ3dpc4IjaQVlPYlX9WR8S2ag7oKy0zK11EbM+3NU8DdgOXRsQBSWeTlox5N/A1YEFEfBhA0jzgpYiYqrQk2OOS1kTEjhqKcA4wgXSFvB34aUScL+nLwLXAV4AfkkbfHpM0mrQA9IQe8h0PXAyMAP4iaVE+j0l5RA9Js4CzSetPClgpaTqwjzRr+7mkvnkjaRZ+SB8O8yPiWUnvIX04zMz7xgGXRER7Df8HM+uGpOHANGBF4bGxoT285yxSXzEqb1oraXoelesVB2dmdqxUerrBwB2SpgDtpGCjM7OAyYUHb08kBTm1BGfNEbELQNJzwJq8/WlScAVwCTCx4ydBwQAAAjdJREFU0CG/SdKIiHi5m3wfzot/H5S0G3hLF+cxi7T8DcDwfB4jgAciYn8u14P5d08fDiscmJnVzQBgb+XiqpcuA56IiMpo/SrgvYCDMzNrXJLOJAViu0nPnr1IGs0aABzo6m2kWwur+6EIBwt/Hy68PsyRfnEA8L5KsFRDvu103scKuDkilrxmo3R9F3n29OHwShXlM7MqRMR/Je2QNCciVuRvXU6OiNZu3vY88HlJN5Pa+0XA7dUc18+cmVmplBbIXgzckRdaPhHYFRGHgU8DA3PSl0mjSRWrgS9KGpzzGSfphDoWdQ1wTaHc1Vw5F3V2HlflETEknSHpNOAx4COShuV9H4L04QDskDQnp5ekc2osi5l1Q2nB+A3A2yXtlHQ1MBe4WlIrsAX4aE47VWlR+TnAEklbcjb3AM+RRuJbgdaIeLCacnjkzMzK8AZJLaRbmIeAXwK35n13Avfm4GMdR0aCNgOHcofYRHoGbAywMV+9/huYXccyXwf8WNJmUl/5KDC/2kwiYo+kx5XmTVoVETdImgBsyLcp24BPRUSzpJWkzvwfwJPASzmbucAiSd8g/Q+X5XRm1o8i4ooudh01vUZENHPkubLi9nagqi8sdaR04WpmZseapOER0SbpjaRgcF5EbDzW5TKzcnnkzMyscdwlaSJpjqS7HZiZvT555MzMzMysgfgLAWZmZmYNxMGZmZmZWQNxcGZmZmbWQBycmZmZmTUQB2dmZmZmDcTBmZmZmVkD+T+7Nw/aaRUbSgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.sort_values('date_time')\n",
    "df['date_time_int'] = df.date_time.astype(np.int64)\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "a = df.loc[df['anomaly1'] == 1, ['date_time_int', 'price_usd']] #anomaly\n",
    "\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label='Normal')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label='Anomaly')\n",
    "plt.xlabel('Date Time Integer')\n",
    "plt.ylabel('price in USD')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualization of anomaly with re-partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['anomaly1'] == 0, 'price_usd']\n",
    "b = df.loc[df['anomaly1'] == 1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10,6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Isolation Forest for anomaly detection.\n",
    "\n",
    "The IsolationForest \"isolates\" observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.\n",
    "\n",
    "This path length, averaged over a forest of such random trees, is a measure of normality and our decision function.\n",
    "\n",
    "Random partitioning produces noticeable shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "scaler = StandardScaler()\n",
    "np_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(np_scaled)\n",
    "# train isolation forest\n",
    "model =  IsolationForest(contamination=outliers_fraction)\n",
    "model.fit(data)\n",
    "\n",
    "df['anomaly2'] = pd.Series(model.predict(data))\n",
    "# df['anomaly2'] = df['anomaly2'].map( {1: 0, -1: 1} )\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "a = df.loc[df['anomaly2'] == -1, ['date_time_int', 'price_usd']] #anomaly\n",
    "\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label = 'Normal')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label = 'Anomaly')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, -1], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['anomaly2'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualisation of anomaly with avg price repartition\n",
    "a = df.loc[df['anomaly2'] == 1, 'price_usd']\n",
    "b = df.loc[df['anomaly2'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10,6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine-Based Anomaly Detection\n",
    "\n",
    "A support vector machine is another effective technique for detecting anomalies. A SVM is typically associated with supervised learning, but OneClassSVM can be used to identify anomalies as an unsupervised problems.\n",
    "\n",
    "### One class SVM\n",
    "\n",
    "Unsupervised Outlier Detection.\n",
    "\n",
    "Estimate the support of a high-dimensional distribution.\n",
    "\n",
    "The implementation is based on libsvm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[['price_usd', 'srch_booking_window', 'srch_saturday_night_bool']]\n",
    "scaler = StandardScaler()\n",
    "np_scaled = scaler.fit_transform(data)\n",
    "data = pd.DataFrame(np_scaled)\n",
    "# train oneclassSVM \n",
    "model = OneClassSVM(nu=outliers_fraction, kernel=\"rbf\", gamma=0.01)\n",
    "model.fit(data)\n",
    " \n",
    "df['anomaly3'] = pd.Series(model.predict(data))\n",
    "# df['anomaly3'] = df['anomaly3'].map( {1: 0, -1: 1} )\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "a = df.loc[df['anomaly3'] == -1, ['date_time_int', 'price_usd']] #anomaly\n",
    "\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label ='Normal')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label = 'Anomaly')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAFpCAYAAACvaj13AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFHtJREFUeJzt3V+M5eV93/HPt6z/pE5i/i0I7eJiy6vIvqgxWmEqeuFAGtnEClwYyVFaVghpb2jkKKkS4psoVSPZNyFFrVCRcbOOHMeIxGVloTaIP0p6YeIlYPwHW2yQBaul7Kb8SVwUVyTfXsyz9nR3vHPYmWdnZuf1kkbn/J7zzJxnfgOz7/md3zmnujsAAKyvf7LRCwAAOBeJLACACUQWAMAEIgsAYAKRBQAwgcgCAJhAZAEATCCyAAAmEFkAABMsFFlVdX5V3V9V36mqZ6rqX1TVhVX1UFU9Oy4vGHOrqu6qqsNV9XRVXTX3WwAA2HxqkbfVqaoDSf6iuz9bVW9N8k+TfCrJy9396aq6I8kF3f2bVXVDkl9JckOSDyX5j939odN9/YsvvrivuOKKNX4rAADzPfHEE3/T3TtXm7dqZFXVTyf5epL39LLJVfXdJB/u7her6rIkj3X3z1TVfxnXv3jyvB93H3v37u1Dhw4t9I0BAGykqnqiu/euNm+Rhwvfk+R4kv9aVU9W1Wer6h1JLj0RTuPykjF/V5IXln3+kTEGALBtLBJZO5JcleTu7v5gkv+T5I7TzK8Vxk45XFZV+6vqUFUdOn78+EKLBQDYKhaJrCNJjnT342P7/ixF10vjYcKMy2PL5l++7PN3Jzl68hft7nu6e2937925c9WHNQEAtpRVI6u7/1eSF6rqZ8bQ9Um+neRgkn1jbF+SB8b1g0luGc8yvCbJa6c7HwsA4Fy0Y8F5v5LkC+OZhc8luTVLgXZfVd2W5PkkN4+5D2bpmYWHk7w+5gIAbCsLRVZ3P5VkpbPor19hbie5fY3rAgDY0rziOwDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATLPpipGy0WuktId+EPuXtIwGAiRzJAgCYwJGsLaKytiNRjmMBwNnlSBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADDBQpFVVd+rqm9U1VNVdWiMXVhVD1XVs+PygjFeVXVXVR2uqqer6qqZ3wAAwGb0Zo5k/Wx3X9nde8f2HUke7u49SR4e20ny0SR7xsf+JHev12IBALaKtTxceGOSA+P6gSQ3LRv/fC/5apLzq+qyNdwPAMCWs2hkdZI/q6onqmr/GLu0u19MknF5yRjfleSFZZ97ZIwBAGwbOxacd213H62qS5I8VFXfOc3cWmGsT5m0FGv7k+Rd73rXgssAANgaFjqS1d1Hx+WxJF9OcnWSl048DDguj43pR5JcvuzTdyc5usLXvKe793b33p07d575dwAAsAmtGllV9Y6q+qkT15P8fJJvJjmYZN+Yti/JA+P6wSS3jGcZXpPktRMPKwIAbBeLPFx4aZIvV9WJ+X/U3f+9qr6W5L6qui3J80luHvMfTHJDksNJXk9y67qvGgBgk1s1srr7uSQfWGH8fye5foXxTnL7uqwOAGCL8orvAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAAT7NjoBXB2VK3t87vXZx0AsF04kgUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEywcGRV1XlV9WRVfWVsv7uqHq+qZ6vqS1X11jH+trF9eNx+xZylAwBsXm/mSNYnkzyzbPszSe7s7j1JXkly2xi/Lckr3f3eJHeOeQAA28pCkVVVu5P8QpLPju1Kcl2S+8eUA0luGtdvHNsZt18/5gMAbBuLHsn6/SS/keQfx/ZFSV7t7jfG9pEku8b1XUleSJJx+2tj/v+nqvZX1aGqOnT8+PEzXD4AwOa0amRV1ceSHOvuJ5YPrzC1F7jtRwPd93T33u7eu3PnzoUWCwCwVexYYM61SX6xqm5I8vYkP52lI1vnV9WOcbRqd5KjY/6RJJcnOVJVO5K8M8nL675yAIBNbNUjWd39W929u7uvSPKJJI909y8neTTJx8e0fUkeGNcPju2M2x/p7lOOZAEAnMvW8jpZv5nk16rqcJbOubp3jN+b5KIx/mtJ7ljbEgEAtp5FHi78oe5+LMlj4/pzSa5eYc7fJ7l5HdYGALBlecV3AIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBggh0bvQBYTdXaPr97fdYBAG+GI1kAABOILACACUQWAMAEzsni7FjTiVVOqgJg6xFZnBUllADYZjxcCAAwgSNZLGatr6PgSBYA24zIYiEe7gOAN8fDhQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACZYNbKq6u1V9ZdV9fWq+lZV/c4Yf3dVPV5Vz1bVl6rqrWP8bWP78Lj9irnfAgDA5rPIkawfJLmuuz+Q5MokH6mqa5J8Jsmd3b0nyStJbhvzb0vySne/N8mdYx4AwLayamT1ku+PzbeMj05yXZL7x/iBJDeN6zeO7Yzbr69a8xvfAQBsKQudk1VV51XVU0mOJXkoyV8nebW73xhTjiTZNa7vSvJCkozbX0ty0XouGgBgs1sosrr7H7r7yiS7k1yd5H0rTRuXKx21OuXdhatqf1UdqqpDx48fX3S9AABbwpt6dmF3v5rksSTXJDm/qnaMm3YnOTquH0lyeZKM29+Z5OUVvtY93b23u/fu3LnzzFYPALBJLfLswp1Vdf64/hNJfi7JM0keTfLxMW1fkgfG9YNjO+P2R7r7lCNZAADnsh2rT8llSQ5U1XlZirL7uvsrVfXtJH9cVf8hyZNJ7h3z703yh1V1OEtHsD4xYd0AAJvaqpHV3U8n+eAK489l6fysk8f/PsnN67I6AIAtyiu+AwBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACYQWQAAE4gsAIAJRBYAwAQiCwBgApEFADCByAIAmGDHRi8AZqta2+d3r886ANheHMkCAJhAZAEATCCyAAAmEFkAABOILACACUQWAMAEIgsAYAKRBQAwgcgCAJhAZAEATCCyAAAmEFkAABOILACACUQWAMAEIgsAYAKRBQAwgcgCAJhAZAEATCCyAAAmEFkAABOsGllVdXlVPVpVz1TVt6rqk2P8wqp6qKqeHZcXjPGqqruq6nBVPV1VV83+JgAANptFjmS9keTXu/t9Sa5JcntVvT/JHUke7u49SR4e20ny0SR7xsf+JHev+6oBADa5VSOru1/s7r8a1/8uyTNJdiW5McmBMe1AkpvG9RuTfL6XfDXJ+VV12bqvHABgE3tT52RV1RVJPpjk8SSXdveLyVKIJblkTNuV5IVln3ZkjAEAbBsLR1ZV/WSSP0nyq939t6ebusJYr/D19lfVoao6dPz48UWXAQCwJSwUWVX1liwF1he6+0/H8EsnHgYcl8fG+JEkly/79N1Jjp78Nbv7nu7e2917d+7ceabrBwDYlBZ5dmEluTfJM939e8tuOphk37i+L8kDy8ZvGc8yvCbJayceVgQA2C52LDDn2iT/Jsk3quqpMfapJJ9Ocl9V3Zbk+SQ3j9seTHJDksNJXk9y67quGABgC1g1srr7f2bl86yS5PoV5neS29e4rnNP/bhduKhTTmsDADYxr/gOADCByAIAmGCRc7JgW1vrI73tkV6AbcmRLACACRzJOkvKiesAsK04kgUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGCCVSOrqj5XVceq6pvLxi6sqoeq6tlxecEYr6q6q6oOV9XTVXXVzMXDdlC1tg8ANsYiR7L+IMlHThq7I8nD3b0nycNjO0k+mmTP+Nif5O71WSYAwNayamR1958nefmk4RuTHBjXDyS5adn453vJV5OcX1WXrddiYStyJApgezrTc7Iu7e4Xk2RcXjLGdyV5Ydm8I2MMAGBbWe8T31f6u7tXnFi1v6oOVdWh48ePr/MyAAA21plG1ksnHgYcl8fG+JEkly+btzvJ0ZW+QHff0917u3vvzp07z3AZAACb05lG1sEk+8b1fUkeWDZ+y3iW4TVJXjvxsCIAwHayY7UJVfXFJB9OcnFVHUny20k+neS+qrotyfNJbh7TH0xyQ5LDSV5PcuuENQMAbHqrRlZ3/9KPuen6FeZ2ktvXuigAgK3OK74DAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJtix0QsA5qrauPvu3rj7BthojmQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEwgsgAAJhBZAAATiCwAgAlEFgDABCILAGACkQUAMIHIAgCYQGQBAEywY6MXAJy7qtb2+d1b+/6B7c2RLACACUQWAMAEIgsAYAKRBQAwgcgCAJhAZAEATCCyAAAm8DpZwKa11te52uj79zpbsL1NOZJVVR+pqu9W1eGqumPGfQAAbGbrfiSrqs5L8p+T/KskR5J8raoOdve31/u+ADazjTwS5ygabLwZR7KuTnK4u5/r7v+b5I+T3DjhfgA4V1Wt7QM2gRnnZO1K8sKy7SNJPjThft4cJ1cA28iaf+Vli4fKRoeWfzPO3Dn07/WMyFpp75zyHVfV/iT7x+b3q+q7E9ay3MVJ/uaMP3uj/4fd2ta271kL+35jbPn9voV/422Ofb89/83YTvv+ny0yaUZkHUly+bLt3UmOnjypu+9Jcs+E+19RVR3q7r1n6/74Eft+49j3G8N+3zj2/cax708145ysryXZU1Xvrqq3JvlEkoMT7gcAYNNa9yNZ3f1GVf3bJP8jyXlJPtfd31rv+wEA2MymvBhpdz+Y5MEZX3sNztpDk5zCvt849v3GsN83jn2/cez7k1RvorPwAQDOFd67EABggm0RWd7mZ66q+lxVHauqby4bu7CqHqqqZ8flBWO8ququ8bN4uqqu2riVb21VdXlVPVpVz1TVt6rqk2Pcvp+sqt5eVX9ZVV8f+/53xvi7q+rxse+/NJ78k6p629g+PG6/YiPXv9VV1XlV9WRVfWVs2+9nQVV9r6q+UVVPVdWhMeb3zWmc85G17G1+Pprk/Ul+qarev7GrOuf8QZKPnDR2R5KHu3tPkofHdrL0c9gzPvYnufssrfFc9EaSX+/u9yW5Jsnt479t+36+HyS5rrs/kOTKJB+pqmuSfCbJnWPfv5LktjH/tiSvdPd7k9w55nHmPpnkmWXb9vvZ87PdfeWyl2rw++Y0zvnIirf5ma67/zzJyycN35jkwLh+IMlNy8Y/30u+muT8qrrs7Kz03NLdL3b3X43rf5elf3R2xb6fbuzD74/Nt4yPTnJdkvvH+Mn7/sTP5P4k11dtz1erXKuq2p3kF5J8dmxX7PeN5PfNaWyHyFrpbX52bdBatpNLu/vFZCkGklwyxv08JhgPg3wwyeOx78+K8ZDVU0mOJXkoyV8nebW73xhTlu/fH+77cftrSS46uys+Z/x+kt9I8o9j+6LY72dLJ/mzqnpivGtL4vfNaU15CYdNZqG3+eGs8fNYZ1X1k0n+JMmvdvffnuYPdft+HXX3PyS5sqrOT/LlJO9badq4tO/XQVV9LMmx7n6iqj58YniFqfb7HNd299GquiTJQ1X1ndPMte+zPY5kLfQ2P6y7l04cGh6Xx8a4n8c6qqq3ZCmwvtDdfzqG7fuzqLtfTfJYls6LO7+qTvzxunz//nDfj9vfmVMfYmd11yb5xar6XpZO/bguS0e27PezoLuPjstjWfrD4ur4fXNa2yGyvM3PxjiYZN+4vi/JA8vGbxnPPLkmyWsnDjXz5oxzS+5N8kx3/96ym+z7yapq5ziClar6iSQ/l6Vz4h5N8vEx7eR9f+Jn8vEkj7QXKXzTuvu3unt3d1+Rpd/lj3T3L8d+n66q3lFVP3XiepKfT/LN+H1zWtvixUir6oYs/bVz4m1+fneDl3ROqaovJvlwlt6B/aUkv53kvyW5L8m7kjyf5ObufnmEwX/K0rMRX09ya3cf2oh1b3VV9S+T/EWSb+RH56d8KkvnZdn3E1XVP8/SSb7nZemP1fu6+99X1XuydITlwiRPJvnX3f2Dqnp7kj/M0nlzLyf5RHc/tzGrPzeMhwv/XXd/zH6fb+zjL4/NHUn+qLt/t6ouit83P9a2iCwAgLNtOzxcCABw1oksAIAJRBYAwAQiCwBgApEFADCByAIAmEBkAQBMILIAACb4f85jCY8q/ohlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['anomaly3'] == 1, 'price_usd']\n",
    "b = df.loc[df['anomaly3'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10,6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anomaly Detection using Gaussian Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_class0 = df.loc[df['srch_saturday_night_bool'] == 0, 'price_usd']\n",
    "df_class1 = df.loc[df['srch_saturday_night_bool'] == 1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(1,2)\n",
    "df_class0.hist(ax=axs[0], bins=30)\n",
    "df_class1.hist(ax=axs[1], bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    }
   ],
   "source": [
    "envelope =  EllipticEnvelope(contamination = outliers_fraction) \n",
    "X_train = df_class0.values.reshape(-1,1)\n",
    "envelope.fit(X_train)\n",
    "df_class0 = pd.DataFrame(df_class0)\n",
    "df_class0['deviation'] = envelope.decision_function(X_train)\n",
    "df_class0['anomaly'] = envelope.predict(X_train)\n",
    "\n",
    "envelope =  EllipticEnvelope(contamination = outliers_fraction) \n",
    "X_train = df_class1.values.reshape(-1,1)\n",
    "envelope.fit(X_train)\n",
    "df_class1 = pd.DataFrame(df_class1)\n",
    "df_class1['deviation'] = envelope.decision_function(X_train)\n",
    "df_class1['anomaly'] = envelope.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the price repartition by categories with anomalies\n",
    "a0 = df_class0.loc[df_class0['anomaly'] == 1, 'price_usd']\n",
    "b0 = df_class0.loc[df_class0['anomaly'] == -1, 'price_usd']\n",
    "\n",
    "a2 = df_class1.loc[df_class1['anomaly'] == 1, 'price_usd']\n",
    "b2 = df_class1.loc[df_class1['anomaly'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(1,2)\n",
    "axs[0].hist([a0,b0], bins=32, stacked=True, color=['blue', 'red'])\n",
    "axs[1].hist([a2,b2], bins=32, stacked=True, color=['blue', 'red'])\n",
    "axs[0].set_title(\"Search Non Saturday Night\")\n",
    "axs[1].set_title(\"Search Saturday Night\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add the data to the main \n",
    "df_class = pd.concat([df_class0, df_class1])\n",
    "df['anomaly5'] = df_class['anomaly']\n",
    "# df['anomaly5'] = np.array(df['anomaly22'] == -1).astype(int)\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "a = df.loc[df['anomaly5'] == -1, ('date_time_int', 'price_usd')] #anomaly\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue', label='Normal')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red', label='Anomaly')\n",
    "plt.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['anomaly5'] == 1, 'price_usd']\n",
    "b = df.loc[df['anomaly5'] == -1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(10, 6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Markov Chain\n",
    "\n",
    "Markov chains can measure the probability of a sequence of events happening. This approach builds a Markov chain for the underline process, and when a sequence of events has happened, we can use the Markov Chain to measure the probability of that sequence occurring and use that to detect any rare sequences.\n",
    "\n",
    "In our price anomaly detection project, we need discretize the data points in define states for markov chain. We will take 'price_usd' to define state for this example and define 5 levels of value (very low, low, average, high, very high)/(VL, L, A, H, VH). Then Markov chains would represent by states VL, L, L, A, A, H, H, VH. And each price would be a price from one state to another state. We can build the Markov chain using historical price data and use the chain to calculate sequence probabilities. Then, we can find the probability of any new sequence happening and then mark rare sequences as anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   3048.000\n",
       "mean     111.144\n",
       "std       55.055\n",
       "min        0.120\n",
       "25%       67.000\n",
       "50%      100.000\n",
       "75%      141.000\n",
       "max      536.000\n",
       "Name: price_usd, dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_usd'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train markov model to get transition matrix\n",
    "def getTransitionMatrix (df):\n",
    "    df = np.array(df)\n",
    "    model = msm.estimate_markov_model(df, 1)\n",
    "    return model.transition_matrix\n",
    "\n",
    "# return the success probability of the state change \n",
    "def successProbabilityMetric(state1, state2, transition_matrix):\n",
    "    proba = 0\n",
    "    for k in range(0,len(transition_matrix)):\n",
    "        if (k != (state2-1)):\n",
    "            proba += transition_matrix[state1-1][k]\n",
    "    return 1-proba\n",
    "\n",
    "# return the success probability of the whole sequence\n",
    "def sucessScore(sequence, transition_matrix):\n",
    "    proba = 0 \n",
    "    for i in range(1,len(sequence)):\n",
    "        if(i == 1):\n",
    "            proba = successProbabilityMetric(sequence[i-1], sequence[i], transition_matrix)\n",
    "        else:\n",
    "            proba = proba*successProbabilityMetric(sequence[i-1], sequence[i], transition_matrix)\n",
    "    return proba\n",
    "\n",
    "# return if the sequence is an anomaly considering a threshold\n",
    "def anomalyElement(sequence, threshold, transition_matrix):\n",
    "    if (sucessScore(sequence, transition_matrix) > threshold):\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "# return a dataframe containing anomaly result for the whole dataset \n",
    "# choosing a sliding windows size (size of sequence to evaluate) and a threshold\n",
    "def markovAnomaly(df, windows_size, threshold):\n",
    "    transition_matrix = getTransitionMatrix(df)\n",
    "    real_threshold = threshold**windows_size\n",
    "    df_anomaly = []\n",
    "    for j in range(0, len(df)):\n",
    "        if (j < windows_size\n",
    "            df_anomaly.append(0)\n",
    "        else:\n",
    "            sequence = df[j-windows_size:j]\n",
    "            sequence = sequence.reset_index(drop=True)\n",
    "            df_anomaly.append(anomalyElement(sequence, real_threshold, transition_matrix))\n",
    "    return df_anomaly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2998\n",
      "1      50\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# definition of the different state\n",
    "x1 = (df['price_usd'] <=55).astype(int)\n",
    "x2= ((df['price_usd'] > 55) & (df['price_usd']<=200)).astype(int)\n",
    "x3 = ((df['price_usd'] > 200) & (df['price_usd']<=300)).astype(int)\n",
    "x4 = ((df['price_usd'] > 300) & (df['price_usd']<=400)).astype(int)\n",
    "x5 = (df['price_usd'] >400).astype(int)\n",
    "df_mm = x1 + 2*x2 + 3*x3 + 4*x4 + 5*x5\n",
    "\n",
    "# getting the anomaly labels for our dataset (evaluating sequence of 5 values and anomaly = less than 20% probable)\n",
    "df_anomaly = markovAnomaly(df_mm, 5, 0.20)\n",
    "df_anomaly = pd.Series(df_anomaly)\n",
    "print(df_anomaly.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['anomaly24'] = df_anomaly\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "a = df.loc[df['anomaly24'] == 1, ('date_time_int', 'price_usd')] #anomaly\n",
    "\n",
    "ax.plot(df['date_time_int'], df['price_usd'], color='blue')\n",
    "ax.scatter(a['date_time_int'],a['price_usd'], color='red')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SusanLi\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.loc[df['anomaly24'] == 0, 'price_usd']\n",
    "b = df.loc[df['anomaly24'] == 1, 'price_usd']\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(16,6))\n",
    "axs.hist([a,b], bins=32, stacked=True, color=['blue', 'red'])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because our anomaly detection is unsupervised learning.  After building the models, we have no idea how well it is doing as we have nothing to test it against. Hence, the results of those methods need to be tested in the field before placing them in the critical path."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
